##################################
# Loading R libraries
##################################
library(DALEX)
library(caret)
library(randomForest)
library(e1071)
library(gbm)
library(skimr)
library(corrplot)
library(lares)
library(dplyr)
library(minerva)
library(CORElearn)
library(patchwork)
##################################
# Loading source and
# formulating the analysis set
##################################
LED <- read.csv("Life_Expectancy_Data.csv",
na.strings=c("NA","NaN"," ",""),
stringsAsFactors = FALSE)
LED <- as.data.frame(LED)
##################################
# Performing a general exploration of the data set
##################################
dim(LED)## [1] 396 22
str(LED)## 'data.frame': 396 obs. of 22 variables:
## $ COUNTRY: chr "Afghanistan" "Albania" "Algeria" "Angola" ...
## $ YEAR : int 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 ...
## $ GENDER : chr "Female" "Female" "Female" "Female" ...
## $ LIFEXP : num 66.4 80.2 78.1 64 78.1 ...
## $ UNEMPR : num 14.06 11.32 18.63 7.84 8.26 ...
## $ INFMOR : num 42.9 7.7 18.6 44.5 5.1 ...
## $ GDP : num 1.88e+10 1.54e+10 1.72e+11 8.94e+10 1.69e+09 ...
## $ GNI : num 1.91e+10 1.52e+10 1.68e+11 8.19e+10 1.58e+09 ...
## $ CLTECH : num 36 80.7 99.3 49.6 100 ...
## $ PERCAP : num 494 5396 3990 2810 17377 ...
## $ RTIMOR : num 15.9 11.7 20.9 26.1 0 ...
## $ TUBINC : num 189 16 61 351 0 29 26 2.2 6.9 6 ...
## $ DPTIMM : num 66 99 91 57 95 ...
## $ HEPIMM : num 66 99 91 53 99 ...
## $ MEAIMM : num 64 95 80 51 93 ...
## $ HOSBED : num 0.432 3.052 1.8 0.8 2.581 ...
## $ SANSER : num 49 99.2 86.1 51.4 85.5 ...
## $ TUBTRT : num 91 88 86 69 72.3 ...
## $ URBPOP : num 25.8 61.2 73.2 66.2 24.5 ...
## $ RURPOP : num 74.2 38.8 26.8 33.8 75.5 ...
## $ NCOMOR : num 36.2 6 12.8 19.4 17.6 ...
## $ SUIRAT : num 3.6 2.7 1.8 2.3 0.8 ...
summary(LED)## COUNTRY YEAR GENDER LIFEXP
## Length:396 Min. :2019 Length:396 Min. :51.20
## Class :character 1st Qu.:2019 Class :character 1st Qu.:67.62
## Mode :character Median :2019 Mode :character Median :74.38
## Mean :2019 Mean :73.10
## 3rd Qu.:2019 3rd Qu.:79.31
## Max. :2019 Max. :88.10
## UNEMPR INFMOR GDP GNI
## Min. : 0.071 Min. : 1.40 Min. :1.884e+08 Min. :3.754e+08
## 1st Qu.: 3.575 1st Qu.: 5.90 1st Qu.:1.131e+10 1st Qu.:1.114e+10
## Median : 5.647 Median :15.05 Median :3.916e+10 Median :4.007e+10
## Mean : 7.749 Mean :21.46 Mean :5.762e+11 Mean :5.972e+11
## 3rd Qu.: 9.839 3rd Qu.:30.38 3rd Qu.:2.500e+11 3rd Qu.:2.460e+11
## Max. :41.153 Max. :88.80 Max. :2.320e+13 Max. :2.340e+13
## CLTECH PERCAP RTIMOR TUBINC
## Min. : 0.00 Min. : 228.2 Min. : 0.00 Min. : 0
## 1st Qu.: 33.50 1st Qu.: 2229.9 1st Qu.: 8.20 1st Qu.: 12
## Median : 80.10 Median : 6617.1 Median :15.95 Median : 46
## Mean : 65.83 Mean : 16917.8 Mean :16.98 Mean :103
## 3rd Qu.:100.00 3rd Qu.: 19575.8 3rd Qu.:23.90 3rd Qu.:140
## Max. :100.00 Max. :175813.9 Max. :64.60 Max. :654
## DPTIMM HEPIMM MEAIMM HOSBED
## Min. :35.00 Min. :35.00 Min. :37.00 Min. : 0.200
## 1st Qu.:85.69 1st Qu.:81.31 1st Qu.:84.85 1st Qu.: 1.300
## Median :92.00 Median :91.00 Median :92.00 Median : 2.572
## Mean :87.90 Mean :86.65 Mean :87.22 Mean : 2.987
## 3rd Qu.:97.00 3rd Qu.:96.00 3rd Qu.:96.00 3rd Qu.: 3.746
## Max. :99.00 Max. :99.00 Max. :99.00 Max. :13.710
## SANSER TUBTRT URBPOP RURPOP
## Min. : 8.632 Min. : 0.00 Min. : 13.25 Min. : 0.00
## 1st Qu.: 63.898 1st Qu.: 73.00 1st Qu.: 41.61 1st Qu.:21.90
## Median : 91.239 Median : 82.00 Median : 58.90 Median :41.10
## Mean : 77.606 Mean : 77.66 Mean : 59.21 Mean :40.79
## 3rd Qu.: 98.648 3rd Qu.: 88.00 3rd Qu.: 78.10 3rd Qu.:58.39
## Max. :100.000 Max. :100.00 Max. :100.00 Max. :86.75
## NCOMOR SUIRAT
## Min. : 4.40 Min. : 0.00
## 1st Qu.:13.60 1st Qu.: 3.30
## Median :19.85 Median : 6.95
## Mean :19.99 Mean : 9.34
## 3rd Qu.:24.02 3rd Qu.:11.22
## Max. :58.40 Max. :63.00
##################################
# Transforming to appropriate data types
##################################
LED$YEAR <- factor(LED$YEAR,
levels = c("2019"))
LED$GENDER <- factor(LED$GENDER,
levels = c("Male","Female"))
##################################
# Reducing the range of values
# for certain numeric predictors
##################################
LED$GDP <- LED$GDP/1000000000
LED$GNI <- LED$GNI/1000000000
LED$PERCAP <- LED$PERCAP/1000
##################################
# Formulating a data type assessment summary
##################################
PDA <- LED
(PDA.Summary <- data.frame(
Column.Index=c(1:length(names(PDA))),
Column.Name= names(PDA),
Column.Type=sapply(PDA, function(x) class(x)),
row.names=NULL)
)## Column.Index Column.Name Column.Type
## 1 1 COUNTRY character
## 2 2 YEAR factor
## 3 3 GENDER factor
## 4 4 LIFEXP numeric
## 5 5 UNEMPR numeric
## 6 6 INFMOR numeric
## 7 7 GDP numeric
## 8 8 GNI numeric
## 9 9 CLTECH numeric
## 10 10 PERCAP numeric
## 11 11 RTIMOR numeric
## 12 12 TUBINC numeric
## 13 13 DPTIMM numeric
## 14 14 HEPIMM numeric
## 15 15 MEAIMM numeric
## 16 16 HOSBED numeric
## 17 17 SANSER numeric
## 18 18 TUBTRT numeric
## 19 19 URBPOP numeric
## 20 20 RURPOP numeric
## 21 21 NCOMOR numeric
## 22 22 SUIRAT numeric
##################################
# Loading dataset
##################################
DQA <- LED
##################################
# Formulating an overall data quality assessment summary
##################################
(DQA.Summary <- data.frame(
Column.Index=c(1:length(names(DQA))),
Column.Name= names(DQA),
Column.Type=sapply(DQA, function(x) class(x)),
Row.Count=sapply(DQA, function(x) nrow(DQA)),
NA.Count=sapply(DQA,function(x)sum(is.na(x))),
Fill.Rate=sapply(DQA,function(x)format(round((sum(!is.na(x))/nrow(DQA)),3),nsmall=3)),
row.names=NULL)
)## Column.Index Column.Name Column.Type Row.Count NA.Count Fill.Rate
## 1 1 COUNTRY character 396 0 1.000
## 2 2 YEAR factor 396 0 1.000
## 3 3 GENDER factor 396 0 1.000
## 4 4 LIFEXP numeric 396 0 1.000
## 5 5 UNEMPR numeric 396 0 1.000
## 6 6 INFMOR numeric 396 0 1.000
## 7 7 GDP numeric 396 0 1.000
## 8 8 GNI numeric 396 0 1.000
## 9 9 CLTECH numeric 396 0 1.000
## 10 10 PERCAP numeric 396 0 1.000
## 11 11 RTIMOR numeric 396 0 1.000
## 12 12 TUBINC numeric 396 0 1.000
## 13 13 DPTIMM numeric 396 0 1.000
## 14 14 HEPIMM numeric 396 0 1.000
## 15 15 MEAIMM numeric 396 0 1.000
## 16 16 HOSBED numeric 396 0 1.000
## 17 17 SANSER numeric 396 0 1.000
## 18 18 TUBTRT numeric 396 0 1.000
## 19 19 URBPOP numeric 396 0 1.000
## 20 20 RURPOP numeric 396 0 1.000
## 21 21 NCOMOR numeric 396 0 1.000
## 22 22 SUIRAT numeric 396 0 1.000
##################################
# Listing all Predictors
##################################
DQA.Predictors <- DQA[,!names(DQA) %in% c("COUNTRY","YEAR","LIFEXP")]
##################################
# Listing all numeric Predictors
##################################
DQA.Predictors.Numeric <- DQA.Predictors[,sapply(DQA.Predictors, is.numeric), drop = FALSE]
if (length(names(DQA.Predictors.Numeric))>0) {
print(paste0("There is (are) ",
(length(names(DQA.Predictors.Numeric))),
" numeric descriptor variable(s)."))
} else {
print("There are no numeric descriptor variables.")
}## [1] "There is (are) 18 numeric descriptor variable(s)."
##################################
# Listing all factor Predictors
##################################
DQA.Predictors.Factor <- DQA.Predictors[,sapply(DQA.Predictors, is.factor), drop = FALSE]
if (length(names(DQA.Predictors.Factor))>0) {
print(paste0("There is (are) ",
(length(names(DQA.Predictors.Factor))),
" factor descriptor variable(s)."))
} else {
print("There are no factor descriptor variables.")
}## [1] "There is (are) 1 factor descriptor variable(s)."
##################################
# Formulating a data quality assessment summary for factor Predictors
##################################
if (length(names(DQA.Predictors.Factor))>0) {
##################################
# Formulating a function to determine the first mode
##################################
FirstModes <- function(x) {
ux <- unique(na.omit(x))
tab <- tabulate(match(x, ux))
ux[tab == max(tab)]
}
##################################
# Formulating a function to determine the second mode
##################################
SecondModes <- function(x) {
ux <- unique(na.omit(x))
tab <- tabulate(match(x, ux))
fm = ux[tab == max(tab)]
sm = x[!(x %in% fm)]
usm <- unique(sm)
tabsm <- tabulate(match(sm, usm))
ifelse(is.na(usm[tabsm == max(tabsm)])==TRUE,
return("x"),
return(usm[tabsm == max(tabsm)]))
}
(DQA.Predictors.Factor.Summary <- data.frame(
Column.Name= names(DQA.Predictors.Factor),
Column.Type=sapply(DQA.Predictors.Factor, function(x) class(x)),
Unique.Count=sapply(DQA.Predictors.Factor, function(x) length(unique(x))),
First.Mode.Value=sapply(DQA.Predictors.Factor, function(x) as.character(FirstModes(x)[1])),
Second.Mode.Value=sapply(DQA.Predictors.Factor, function(x) as.character(SecondModes(x)[1])),
First.Mode.Count=sapply(DQA.Predictors.Factor, function(x) sum(na.omit(x) == FirstModes(x)[1])),
Second.Mode.Count=sapply(DQA.Predictors.Factor, function(x) sum(na.omit(x) == SecondModes(x)[1])),
Unique.Count.Ratio=sapply(DQA.Predictors.Factor, function(x) format(round((length(unique(x))/nrow(DQA.Predictors.Factor)),3), nsmall=3)),
First.Second.Mode.Ratio=sapply(DQA.Predictors.Factor, function(x) format(round((sum(na.omit(x) == FirstModes(x)[1])/sum(na.omit(x) == SecondModes(x)[1])),3), nsmall=3)),
row.names=NULL)
)
}## Column.Name Column.Type Unique.Count First.Mode.Value Second.Mode.Value
## 1 GENDER factor 2 Female x
## First.Mode.Count Second.Mode.Count Unique.Count.Ratio First.Second.Mode.Ratio
## 1 198 0 0.005 Inf
##################################
# Formulating a data quality assessment summary for numeric Predictors
##################################
if (length(names(DQA.Predictors.Numeric))>0) {
##################################
# Formulating a function to determine the first mode
##################################
FirstModes <- function(x) {
ux <- unique(na.omit(x))
tab <- tabulate(match(x, ux))
ux[tab == max(tab)]
}
##################################
# Formulating a function to determine the second mode
##################################
SecondModes <- function(x) {
ux <- unique(na.omit(x))
tab <- tabulate(match(x, ux))
fm = ux[tab == max(tab)]
sm = na.omit(x)[!(na.omit(x) %in% fm)]
usm <- unique(sm)
tabsm <- tabulate(match(sm, usm))
ifelse(is.na(usm[tabsm == max(tabsm)])==TRUE,
return(0.00001),
return(usm[tabsm == max(tabsm)]))
}
(DQA.Predictors.Numeric.Summary <- data.frame(
Column.Name= names(DQA.Predictors.Numeric),
Column.Type=sapply(DQA.Predictors.Numeric, function(x) class(x)),
Unique.Count=sapply(DQA.Predictors.Numeric, function(x) length(unique(x))),
Unique.Count.Ratio=sapply(DQA.Predictors.Numeric, function(x) format(round((length(unique(x))/nrow(DQA.Predictors.Numeric)),3), nsmall=3)),
First.Mode.Value=sapply(DQA.Predictors.Numeric, function(x) format(round((FirstModes(x)[1]),3),nsmall=3)),
Second.Mode.Value=sapply(DQA.Predictors.Numeric, function(x) format(round((SecondModes(x)[1]),3),nsmall=3)),
First.Mode.Count=sapply(DQA.Predictors.Numeric, function(x) sum(na.omit(x) == FirstModes(x)[1])),
Second.Mode.Count=sapply(DQA.Predictors.Numeric, function(x) sum(na.omit(x) == SecondModes(x)[1])),
First.Second.Mode.Ratio=sapply(DQA.Predictors.Numeric, function(x) format(round((sum(na.omit(x) == FirstModes(x)[1])/sum(na.omit(x) == SecondModes(x)[1])),3), nsmall=3)),
Minimum=sapply(DQA.Predictors.Numeric, function(x) format(round(min(x,na.rm = TRUE),3), nsmall=3)),
Mean=sapply(DQA.Predictors.Numeric, function(x) format(round(mean(x,na.rm = TRUE),3), nsmall=3)),
Median=sapply(DQA.Predictors.Numeric, function(x) format(round(median(x,na.rm = TRUE),3), nsmall=3)),
Maximum=sapply(DQA.Predictors.Numeric, function(x) format(round(max(x,na.rm = TRUE),3), nsmall=3)),
Skewness=sapply(DQA.Predictors.Numeric, function(x) format(round(skewness(x,na.rm = TRUE),3), nsmall=3)),
Kurtosis=sapply(DQA.Predictors.Numeric, function(x) format(round(kurtosis(x,na.rm = TRUE),3), nsmall=3)),
Percentile25th=sapply(DQA.Predictors.Numeric, function(x) format(round(quantile(x,probs=0.25,na.rm = TRUE),3), nsmall=3)),
Percentile75th=sapply(DQA.Predictors.Numeric, function(x) format(round(quantile(x,probs=0.75,na.rm = TRUE),3), nsmall=3)),
row.names=NULL)
)
}## Column.Name Column.Type Unique.Count Unique.Count.Ratio First.Mode.Value
## 1 UNEMPR numeric 372 0.939 8.256
## 2 INFMOR numeric 245 0.619 30.235
## 3 GDP numeric 193 0.487 1390.000
## 4 GNI numeric 192 0.485 2040.000
## 5 CLTECH numeric 112 0.283 100.000
## 6 PERCAP numeric 197 0.497 12.669
## 7 RTIMOR numeric 142 0.359 18.229
## 8 TUBINC numeric 146 0.369 136.043
## 9 DPTIMM numeric 46 0.116 99.000
## 10 HEPIMM numeric 46 0.116 81.308
## 11 MEAIMM numeric 48 0.121 99.000
## 12 HOSBED numeric 174 0.439 2.986
## 13 SANSER numeric 187 0.472 100.000
## 14 TUBTRT numeric 59 0.149 84.000
## 15 URBPOP numeric 192 0.485 100.000
## 16 RURPOP numeric 192 0.485 0.000
## 17 NCOMOR numeric 216 0.545 22.100
## 18 SUIRAT numeric 177 0.447 10.619
## Second.Mode.Value First.Mode.Count Second.Mode.Count First.Second.Mode.Ratio
## 1 3.924 22 2 11.000
## 2 2.100 28 7 4.000
## 3 18.799 4 2 2.000
## 4 316.000 8 4 2.000
## 5 60.593 110 34 3.235
## 6 0.494 4 2 2.000
## 7 26.800 28 6 4.667
## 8 35.000 12 10 1.200
## 9 85.685 44 30 1.467
## 10 99.000 40 38 1.053
## 11 84.855 48 30 1.600
## 12 0.400 34 8 4.250
## 13 49.006 24 2 12.000
## 14 83.000 22 20 1.100
## 15 55.985 10 4 2.500
## 16 44.015 10 4 2.500
## 17 6.800 30 5 6.000
## 18 7.600 30 8 3.750
## Minimum Mean Median Maximum Skewness Kurtosis Percentile25th
## 1 0.071 7.749 5.648 41.153 1.758 3.711 3.575
## 2 1.400 21.464 15.050 88.800 1.090 0.580 5.900
## 3 0.188 576.200 39.162 23200.000 7.506 59.379 11.315
## 4 0.375 597.197 40.068 23400.000 7.467 59.073 11.141
## 5 0.000 65.833 80.100 100.000 -0.630 -1.132 33.500
## 6 0.228 16.918 6.617 175.814 2.746 10.405 2.230
## 7 0.000 16.977 15.950 64.600 0.747 1.046 8.200
## 8 0.000 102.983 46.000 654.000 1.870 3.203 12.000
## 9 35.000 87.904 92.000 99.000 -1.864 3.470 85.685
## 10 35.000 86.654 91.000 99.000 -1.602 2.510 81.308
## 11 37.000 87.221 92.000 99.000 -1.695 2.607 84.855
## 12 0.200 2.987 2.572 13.710 1.700 3.893 1.300
## 13 8.632 77.606 91.239 100.000 -1.129 -0.139 63.898
## 14 0.000 77.662 82.000 100.000 -2.197 5.606 73.000
## 15 13.250 59.211 58.900 100.000 -0.141 -0.994 41.612
## 16 0.000 40.789 41.100 86.750 0.141 -0.994 21.901
## 17 4.400 19.986 19.850 58.400 0.870 1.558 13.600
## 18 0.000 9.340 6.950 63.000 2.325 7.134 3.300
## Percentile75th
## 1 9.840
## 2 30.376
## 3 250.000
## 4 246.000
## 5 100.000
## 6 19.576
## 7 23.900
## 8 140.000
## 9 97.000
## 10 96.000
## 11 96.000
## 12 3.746
## 13 98.648
## 14 88.000
## 15 78.099
## 16 58.388
## 17 24.025
## 18 11.225
##################################
# Identifying potential data quality issues
##################################
##################################
# Checking for missing observations
##################################
if ((nrow(DQA.Summary[DQA.Summary$NA.Count>0,]))>0){
print(paste0("Missing observations noted for ",
(nrow(DQA.Summary[DQA.Summary$NA.Count>0,])),
" variable(s) with NA.Count>0 and Fill.Rate<1.0."))
DQA.Summary[DQA.Summary$NA.Count>0,]
} else {
print("No missing observations noted.")
}## [1] "No missing observations noted."
##################################
# Checking for zero or near-zero variance Predictors
##################################
if (length(names(DQA.Predictors.Factor))==0) {
print("No factor Predictors noted.")
} else if (nrow(DQA.Predictors.Factor.Summary[as.numeric(as.character(DQA.Predictors.Factor.Summary$First.Second.Mode.Ratio))>5,])>0){
print(paste0("Low variance observed for ",
(nrow(DQA.Predictors.Factor.Summary[as.numeric(as.character(DQA.Predictors.Factor.Summary$First.Second.Mode.Ratio))>5,])),
" factor variable(s) with First.Second.Mode.Ratio>5."))
DQA.Predictors.Factor.Summary[as.numeric(as.character(DQA.Predictors.Factor.Summary$First.Second.Mode.Ratio))>5,]
} else {
print("No low variance factor Predictors due to high first-second mode ratio noted.")
}## [1] "Low variance observed for 1 factor variable(s) with First.Second.Mode.Ratio>5."
## Column.Name Column.Type Unique.Count First.Mode.Value Second.Mode.Value
## 1 GENDER factor 2 Female x
## First.Mode.Count Second.Mode.Count Unique.Count.Ratio First.Second.Mode.Ratio
## 1 198 0 0.005 Inf
if (length(names(DQA.Predictors.Numeric))==0) {
print("No numeric Predictors noted.")
} else if (nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$First.Second.Mode.Ratio))>5,])>0){
print(paste0("Low variance observed for ",
(nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$First.Second.Mode.Ratio))>5,])),
" numeric variable(s) with First.Second.Mode.Ratio>5."))
DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$First.Second.Mode.Ratio))>5,]
} else {
print("No low variance numeric Predictors due to high first-second mode ratio noted.")
}## [1] "Low variance observed for 3 numeric variable(s) with First.Second.Mode.Ratio>5."
## Column.Name Column.Type Unique.Count Unique.Count.Ratio First.Mode.Value
## 1 UNEMPR numeric 372 0.939 8.256
## 13 SANSER numeric 187 0.472 100.000
## 17 NCOMOR numeric 216 0.545 22.100
## Second.Mode.Value First.Mode.Count Second.Mode.Count First.Second.Mode.Ratio
## 1 3.924 22 2 11.000
## 13 49.006 24 2 12.000
## 17 6.800 30 5 6.000
## Minimum Mean Median Maximum Skewness Kurtosis Percentile25th
## 1 0.071 7.749 5.648 41.153 1.758 3.711 3.575
## 13 8.632 77.606 91.239 100.000 -1.129 -0.139 63.898
## 17 4.400 19.986 19.850 58.400 0.870 1.558 13.600
## Percentile75th
## 1 9.840
## 13 98.648
## 17 24.025
if (length(names(DQA.Predictors.Numeric))==0) {
print("No numeric Predictors noted.")
} else if (nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Unique.Count.Ratio))<0.01,])>0){
print(paste0("Low variance observed for ",
(nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Unique.Count.Ratio))<0.01,])),
" numeric variable(s) with Unique.Count.Ratio<0.01."))
DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Unique.Count.Ratio))<0.01,]
} else {
print("No low variance numeric Predictors due to low unique count ratio noted.")
}## [1] "No low variance numeric Predictors due to low unique count ratio noted."
##################################
# Checking for skewed Predictors
##################################
if (length(names(DQA.Predictors.Numeric))==0) {
print("No numeric Predictors noted.")
} else if (nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))>3 |
as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))<(-3),])>0){
print(paste0("High skewness observed for ",
(nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))>3 |
as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))<(-3),])),
" numeric variable(s) with Skewness>3 or Skewness<(-3)."))
DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))>3 |
as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))<(-3),]
} else {
print("No skewed numeric Predictors noted.")
}## [1] "High skewness observed for 2 numeric variable(s) with Skewness>3 or Skewness<(-3)."
## Column.Name Column.Type Unique.Count Unique.Count.Ratio First.Mode.Value
## 3 GDP numeric 193 0.487 1390.000
## 4 GNI numeric 192 0.485 2040.000
## Second.Mode.Value First.Mode.Count Second.Mode.Count First.Second.Mode.Ratio
## 3 18.799 4 2 2.000
## 4 316.000 8 4 2.000
## Minimum Mean Median Maximum Skewness Kurtosis Percentile25th
## 3 0.188 576.200 39.162 23200.000 7.506 59.379 11.315
## 4 0.375 597.197 40.068 23400.000 7.467 59.073 11.141
## Percentile75th
## 3 250.000
## 4 246.000
##################################
# Loading dataset
##################################
DPA <- LED
##################################
# Gathering descriptive statistics
##################################
(DPA_Skimmed <- skim(DPA))| Name | DPA |
| Number of rows | 396 |
| Number of columns | 22 |
| _______________________ | |
| Column type frequency: | |
| character | 1 |
| factor | 2 |
| numeric | 19 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| COUNTRY | 0 | 1 | 4 | 30 | 0 | 198 | 0 |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| YEAR | 0 | 1 | FALSE | 1 | 201: 396 |
| GENDER | 0 | 1 | FALSE | 2 | Mal: 198, Fem: 198 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| LIFEXP | 0 | 1 | 73.10 | 7.82 | 51.20 | 67.62 | 74.38 | 79.31 | 88.10 | ▁▃▆▇▃ |
| UNEMPR | 0 | 1 | 7.75 | 6.34 | 0.07 | 3.58 | 5.65 | 9.84 | 41.15 | ▇▂▁▁▁ |
| INFMOR | 0 | 1 | 21.46 | 18.66 | 1.40 | 5.90 | 15.05 | 30.38 | 88.80 | ▇▃▂▁▁ |
| GDP | 0 | 1 | 576.20 | 2504.77 | 0.19 | 11.31 | 39.16 | 250.00 | 23200.00 | ▇▁▁▁▁ |
| GNI | 0 | 1 | 597.20 | 2530.16 | 0.38 | 11.14 | 40.07 | 246.00 | 23400.00 | ▇▁▁▁▁ |
| CLTECH | 0 | 1 | 65.83 | 36.32 | 0.00 | 33.50 | 80.10 | 100.00 | 100.00 | ▃▁▁▂▇ |
| PERCAP | 0 | 1 | 16.92 | 24.49 | 0.23 | 2.23 | 6.62 | 19.58 | 175.81 | ▇▁▁▁▁ |
| RTIMOR | 0 | 1 | 16.98 | 10.32 | 0.00 | 8.20 | 15.95 | 23.90 | 64.60 | ▇▇▅▁▁ |
| TUBINC | 0 | 1 | 102.98 | 133.53 | 0.00 | 12.00 | 46.00 | 140.00 | 654.00 | ▇▂▁▁▁ |
| DPTIMM | 0 | 1 | 87.90 | 12.38 | 35.00 | 85.69 | 92.00 | 97.00 | 99.00 | ▁▁▁▃▇ |
| HEPIMM | 0 | 1 | 86.65 | 12.69 | 35.00 | 81.31 | 91.00 | 96.00 | 99.00 | ▁▁▁▃▇ |
| MEAIMM | 0 | 1 | 87.22 | 13.14 | 37.00 | 84.85 | 92.00 | 96.00 | 99.00 | ▁▁▁▃▇ |
| HOSBED | 0 | 1 | 2.99 | 2.35 | 0.20 | 1.30 | 2.57 | 3.75 | 13.71 | ▇▅▂▁▁ |
| SANSER | 0 | 1 | 77.61 | 27.61 | 8.63 | 63.90 | 91.24 | 98.65 | 100.00 | ▁▁▁▂▇ |
| TUBTRT | 0 | 1 | 77.66 | 16.93 | 0.00 | 73.00 | 82.00 | 88.00 | 100.00 | ▁▁▁▅▇ |
| URBPOP | 0 | 1 | 59.21 | 23.24 | 13.25 | 41.61 | 58.90 | 78.10 | 100.00 | ▅▆▇▇▆ |
| RURPOP | 0 | 1 | 40.79 | 23.24 | 0.00 | 21.90 | 41.10 | 58.39 | 86.75 | ▆▇▇▆▅ |
| NCOMOR | 0 | 1 | 19.99 | 8.40 | 4.40 | 13.60 | 19.85 | 24.02 | 58.40 | ▅▇▂▁▁ |
| SUIRAT | 0 | 1 | 9.34 | 9.01 | 0.00 | 3.30 | 6.95 | 11.22 | 63.00 | ▇▂▁▁▁ |
##################################
# Outlier Treatment
##################################
##################################
# Listing all Predictors
##################################
DPA.Predictors <- DPA[,!names(DPA) %in% c("COUNTRY","YEAR","LIFEXP")]
##################################
# Listing all numeric predictors
##################################
DPA.Predictors.Numeric <- DPA.Predictors[,sapply(DPA.Predictors, is.numeric)]
##################################
# Identifying outliers for the numeric predictors
##################################
OutlierCountList <- c()
for (i in 1:ncol(DPA.Predictors.Numeric)) {
Outliers <- boxplot.stats(DPA.Predictors.Numeric[,i])$out
OutlierCount <- length(Outliers)
OutlierCountList <- append(OutlierCountList,OutlierCount)
OutlierIndices <- which(DPA.Predictors.Numeric[,i] %in% c(Outliers))
print(
ggplot(DPA.Predictors.Numeric, aes(x=DPA.Predictors.Numeric[,i])) +
geom_boxplot() +
theme_bw() +
theme(axis.text.y=element_blank(),
axis.ticks.y=element_blank()) +
xlab(names(DPA.Predictors.Numeric)[i]) +
labs(title=names(DPA.Predictors.Numeric)[i],
subtitle=paste0(OutlierCount, " Outlier(s) Detected")))
}##################################
# Formulating the histogram
# for the numeric predictors
##################################
for (i in 1:ncol(DPA.Predictors.Numeric)) {
Median <- format(round(median(DPA.Predictors.Numeric[,i],na.rm = TRUE),2), nsmall=2)
Mean <- format(round(mean(DPA.Predictors.Numeric[,i],na.rm = TRUE),2), nsmall=2)
Skewness <- format(round(skewness(DPA.Predictors.Numeric[,i],na.rm = TRUE),2), nsmall=2)
print(
ggplot(DPA.Predictors.Numeric, aes(x=DPA.Predictors.Numeric[,i])) +
geom_histogram(binwidth=1,color="black", fill="white") +
geom_vline(aes(xintercept=mean(DPA.Predictors.Numeric[,i])),
color="blue", size=1) +
geom_vline(aes(xintercept=median(DPA.Predictors.Numeric[,i])),
color="red", size=1) +
theme_bw() +
ylab("Count") +
xlab(names(DPA.Predictors.Numeric)[i]) +
labs(title=names(DPA.Predictors.Numeric)[i],
subtitle=paste0("Median = ", Median,
", Mean = ", Mean,
", Skewness = ", Skewness)))
}##################################
# Investigating distributional anomalies
# observed for several predictors
##################################
(INFMOR_Unique <- DPA %>%
group_by(INFMOR) %>%
summarize(Distinct_INFMOR = n_distinct(COUNTRY)) %>%
arrange(desc(Distinct_INFMOR)) %>%
slice(1:5))## # A tibble: 5 x 2
## INFMOR Distinct_INFMOR
## <dbl> <int>
## 1 30.2 14
## 2 2.1 7
## 3 6.4 6
## 4 1.7 4
## 5 2.5 4
(INFMOR_Unique_Country <- DPA[round(DPA$INFMOR,digits=1)==30.2,c("COUNTRY")])## [1] "Aruba" "Bermuda"
## [3] "Channel Islands" "Faroe Islands"
## [5] "French Polynesia" "Guam"
## [7] "Hong Kong SAR, China" "Kosovo"
## [9] "Liechtenstein" "Macao SAR, China"
## [11] "New Caledonia" "Puerto Rico"
## [13] "St. Martin (French part)" "Virgin Islands (U.S.)"
## [15] "Aruba" "Bermuda"
## [17] "Channel Islands" "Faroe Islands"
## [19] "French Polynesia" "Guam"
## [21] "Hong Kong SAR, China" "Kosovo"
## [23] "Liechtenstein" "Macao SAR, China"
## [25] "New Caledonia" "Puerto Rico"
## [27] "St. Martin (French part)" "Virgin Islands (U.S.)"
DPA %>%
group_by(CLTECH) %>%
summarize(Distinct_CLTECH = n_distinct(COUNTRY)) %>%
arrange(desc(Distinct_CLTECH)) %>%
slice(1:5)## # A tibble: 5 x 2
## CLTECH Distinct_CLTECH
## <dbl> <int>
## 1 100 55
## 2 60.6 17
## 3 9.30 3
## 4 99.9 3
## 5 0.2 2
(CLTECH_Unique_Country <- DPA[round(DPA$CLTECH,digits=1)==60.6,c("COUNTRY")])## [1] "Aruba" "Bermuda"
## [3] "Channel Islands" "Faroe Islands"
## [5] "French Polynesia" "Guam"
## [7] "Hong Kong SAR, China" "Kosovo"
## [9] "Lebanon" "Libya"
## [11] "Liechtenstein" "Macao SAR, China"
## [13] "New Caledonia" "Puerto Rico"
## [15] "St. Martin (French part)" "Virgin Islands (U.S.)"
## [17] "West Bank and Gaza" "Aruba"
## [19] "Bermuda" "Channel Islands"
## [21] "Faroe Islands" "French Polynesia"
## [23] "Guam" "Hong Kong SAR, China"
## [25] "Kosovo" "Lebanon"
## [27] "Libya" "Liechtenstein"
## [29] "Macao SAR, China" "New Caledonia"
## [31] "Puerto Rico" "St. Martin (French part)"
## [33] "Virgin Islands (U.S.)" "West Bank and Gaza"
DPA %>%
group_by(RTIMOR) %>%
summarize(Distinct_RTIMOR = n_distinct(COUNTRY)) %>%
arrange(desc(Distinct_RTIMOR)) %>%
slice(1:5)## # A tibble: 5 x 2
## RTIMOR Distinct_RTIMOR
## <dbl> <int>
## 1 18.2 14
## 2 3.9 3
## 3 5.1 3
## 4 5.3 3
## 5 12.7 3
(RTIMOR_Unique_Country <- DPA[round(DPA$RTIMOR,digits=1)==18.2,c("COUNTRY")])## [1] "Aruba" "Bermuda"
## [3] "Channel Islands" "Faroe Islands"
## [5] "French Polynesia" "Guam"
## [7] "Hong Kong SAR, China" "Kosovo"
## [9] "Liechtenstein" "Macao SAR, China"
## [11] "New Caledonia" "Puerto Rico"
## [13] "St. Martin (French part)" "Virgin Islands (U.S.)"
## [15] "Aruba" "Bermuda"
## [17] "Channel Islands" "Faroe Islands"
## [19] "French Polynesia" "Guam"
## [21] "Hong Kong SAR, China" "Kosovo"
## [23] "Liechtenstein" "Macao SAR, China"
## [25] "New Caledonia" "Puerto Rico"
## [27] "St. Martin (French part)" "Virgin Islands (U.S.)"
DPA %>%
group_by(DPTIMM) %>%
summarize(Distinct_DPTIMM = n_distinct(COUNTRY)) %>%
arrange(desc(Distinct_DPTIMM)) %>%
slice(1:5)## # A tibble: 5 x 2
## DPTIMM Distinct_DPTIMM
## <dbl> <int>
## 1 99 22
## 2 85.7 15
## 3 97 14
## 4 98 14
## 5 95 13
(DPTIMM_Unique_Country <- DPA[round(DPA$DPTIMM,digits=1)==85.7,c("COUNTRY")])## [1] "Aruba" "Bermuda"
## [3] "Channel Islands" "Faroe Islands"
## [5] "French Polynesia" "Guam"
## [7] "Hong Kong SAR, China" "Kosovo"
## [9] "Liechtenstein" "Macao SAR, China"
## [11] "New Caledonia" "Puerto Rico"
## [13] "St. Martin (French part)" "Virgin Islands (U.S.)"
## [15] "West Bank and Gaza" "Aruba"
## [17] "Bermuda" "Channel Islands"
## [19] "Faroe Islands" "French Polynesia"
## [21] "Guam" "Hong Kong SAR, China"
## [23] "Kosovo" "Liechtenstein"
## [25] "Macao SAR, China" "New Caledonia"
## [27] "Puerto Rico" "St. Martin (French part)"
## [29] "Virgin Islands (U.S.)" "West Bank and Gaza"
DPA %>%
group_by(HEPIMM) %>%
summarize(Distinct_HEPIMM = n_distinct(COUNTRY)) %>%
arrange(desc(Distinct_HEPIMM)) %>%
slice(1:5)## # A tibble: 5 x 2
## HEPIMM Distinct_HEPIMM
## <dbl> <int>
## 1 81.3 20
## 2 99 19
## 3 97 17
## 4 98 11
## 5 92 10
(HEPIMM_Unique_Country <- DPA[round(DPA$HEPIMM,digits=1)==81.3,c("COUNTRY")])## [1] "Aruba" "Bermuda"
## [3] "Channel Islands" "Denmark"
## [5] "Faroe Islands" "Finland"
## [7] "French Polynesia" "Guam"
## [9] "Hong Kong SAR, China" "Hungary"
## [11] "Iceland" "Kosovo"
## [13] "Liechtenstein" "Macao SAR, China"
## [15] "New Caledonia" "Puerto Rico"
## [17] "Slovenia" "St. Martin (French part)"
## [19] "Virgin Islands (U.S.)" "West Bank and Gaza"
## [21] "Aruba" "Bermuda"
## [23] "Channel Islands" "Denmark"
## [25] "Faroe Islands" "Finland"
## [27] "French Polynesia" "Guam"
## [29] "Hong Kong SAR, China" "Hungary"
## [31] "Iceland" "Kosovo"
## [33] "Liechtenstein" "Macao SAR, China"
## [35] "New Caledonia" "Puerto Rico"
## [37] "Slovenia" "St. Martin (French part)"
## [39] "Virgin Islands (U.S.)" "West Bank and Gaza"
DPA %>%
group_by(MEAIMM) %>%
summarize(Distinct_MEAIMM = n_distinct(COUNTRY)) %>%
arrange(desc(Distinct_MEAIMM)) %>%
slice(1:5)## # A tibble: 5 x 2
## MEAIMM Distinct_MEAIMM
## <dbl> <int>
## 1 99 24
## 2 84.9 15
## 3 95 14
## 4 96 14
## 5 98 13
(MEAIMM_Unique_Country <- DPA[round(DPA$MEAIMM,digits=1)==84.9,c("COUNTRY")])## [1] "Aruba" "Bermuda"
## [3] "Channel Islands" "Faroe Islands"
## [5] "French Polynesia" "Guam"
## [7] "Hong Kong SAR, China" "Kosovo"
## [9] "Liechtenstein" "Macao SAR, China"
## [11] "New Caledonia" "Puerto Rico"
## [13] "St. Martin (French part)" "Virgin Islands (U.S.)"
## [15] "West Bank and Gaza" "Aruba"
## [17] "Bermuda" "Channel Islands"
## [19] "Faroe Islands" "French Polynesia"
## [21] "Guam" "Hong Kong SAR, China"
## [23] "Kosovo" "Liechtenstein"
## [25] "Macao SAR, China" "New Caledonia"
## [27] "Puerto Rico" "St. Martin (French part)"
## [29] "Virgin Islands (U.S.)" "West Bank and Gaza"
DPA %>%
group_by(HOSBED) %>%
summarize(Distinct_HOSBED = n_distinct(COUNTRY)) %>%
arrange(desc(Distinct_HOSBED)) %>%
slice(1:5)## # A tibble: 5 x 2
## HOSBED Distinct_HOSBED
## <dbl> <int>
## 1 2.99 17
## 2 0.4 4
## 3 0.8 2
## 4 0.85 2
## 5 0.9 2
(HOSBED_Unique_Country <- DPA[round(DPA$HOSBED,digits=1)==3.0,c("COUNTRY")])## [1] "Aruba" "Bermuda"
## [3] "Channel Islands" "Faroe Islands"
## [5] "French Polynesia" "Guam"
## [7] "Hong Kong SAR, China" "Kosovo"
## [9] "Liechtenstein" "Macao SAR, China"
## [11] "Namibia" "New Caledonia"
## [13] "Papua New Guinea" "Puerto Rico"
## [15] "South Sudan" "St. Martin (French part)"
## [17] "Virgin Islands (U.S.)" "West Bank and Gaza"
## [19] "Aruba" "Bermuda"
## [21] "Channel Islands" "Faroe Islands"
## [23] "French Polynesia" "Guam"
## [25] "Hong Kong SAR, China" "Kosovo"
## [27] "Liechtenstein" "Macao SAR, China"
## [29] "Namibia" "New Caledonia"
## [31] "Papua New Guinea" "Puerto Rico"
## [33] "South Sudan" "St. Martin (French part)"
## [35] "Virgin Islands (U.S.)" "West Bank and Gaza"
DPA %>%
group_by(NCOMOR) %>%
summarize(Distinct_NCOMOR = n_distinct(COUNTRY)) %>%
arrange(desc(Distinct_NCOMOR)) %>%
slice(1:5)## # A tibble: 5 x 2
## NCOMOR Distinct_NCOMOR
## <dbl> <int>
## 1 22.1 15
## 2 6.8 5
## 3 13.6 5
## 4 15.2 5
## 5 17.5 5
(NCOMOR_Unique_Country <- DPA[round(DPA$NCOMOR,digits=1)==22.1,c("COUNTRY")])## [1] "Aruba" "Bermuda"
## [3] "Burkina Faso" "Channel Islands"
## [5] "Faroe Islands" "French Polynesia"
## [7] "Guam" "Hong Kong SAR, China"
## [9] "Kosovo" "Liechtenstein"
## [11] "Macao SAR, China" "New Caledonia"
## [13] "Puerto Rico" "St. Martin (French part)"
## [15] "Virgin Islands (U.S.)" "West Bank and Gaza"
## [17] "Aruba" "Bermuda"
## [19] "Channel Islands" "Dominican Republic"
## [21] "Equatorial Guinea" "Estonia"
## [23] "Faroe Islands" "French Polynesia"
## [25] "Guam" "Hong Kong SAR, China"
## [27] "Kosovo" "Liechtenstein"
## [29] "Macao SAR, China" "New Caledonia"
## [31] "Puerto Rico" "Sierra Leone"
## [33] "St. Martin (French part)" "Virgin Islands (U.S.)"
## [35] "West Bank and Gaza"
DPA %>%
group_by(SUIRAT) %>%
summarize(Distinct_SUIRAT = n_distinct(COUNTRY)) %>%
arrange(desc(Distinct_SUIRAT)) %>%
slice(1:5)## # A tibble: 5 x 2
## SUIRAT Distinct_SUIRAT
## <dbl> <int>
## 1 10.6 15
## 2 7.6 8
## 3 1.7 7
## 4 2 7
## 5 2.8 7
(SUIRAT_Unique_Country <- DPA[round(DPA$SUIRAT,digits=1)==10.6,c("COUNTRY")])## [1] "Aruba" "Bermuda"
## [3] "Channel Islands" "Faroe Islands"
## [5] "French Polynesia" "Guam"
## [7] "Hong Kong SAR, China" "Kosovo"
## [9] "Liechtenstein" "Macao SAR, China"
## [11] "New Caledonia" "Puerto Rico"
## [13] "St. Martin (French part)" "Virgin Islands (U.S.)"
## [15] "West Bank and Gaza" "Aruba"
## [17] "Bermuda" "Channel Islands"
## [19] "Congo, Dem. Rep." "Faroe Islands"
## [21] "French Polynesia" "Guam"
## [23] "Hong Kong SAR, China" "Kosovo"
## [25] "Liechtenstein" "Macao SAR, China"
## [27] "New Caledonia" "Puerto Rico"
## [29] "St. Martin (French part)" "Virgin Islands (U.S.)"
## [31] "West Bank and Gaza"
(AnomalousVariables_Unique_Country <- MEAIMM_Unique_Country)## [1] "Aruba" "Bermuda"
## [3] "Channel Islands" "Faroe Islands"
## [5] "French Polynesia" "Guam"
## [7] "Hong Kong SAR, China" "Kosovo"
## [9] "Liechtenstein" "Macao SAR, China"
## [11] "New Caledonia" "Puerto Rico"
## [13] "St. Martin (French part)" "Virgin Islands (U.S.)"
## [15] "West Bank and Gaza" "Aruba"
## [17] "Bermuda" "Channel Islands"
## [19] "Faroe Islands" "French Polynesia"
## [21] "Guam" "Hong Kong SAR, China"
## [23] "Kosovo" "Liechtenstein"
## [25] "Macao SAR, China" "New Caledonia"
## [27] "Puerto Rico" "St. Martin (French part)"
## [29] "Virgin Islands (U.S.)" "West Bank and Gaza"
##################################
# Removing associated rows associated
# with anomalous variables
##################################
dim(DPA)## [1] 396 22
DPA <- DPA[!(DPA$COUNTRY %in% AnomalousVariables_Unique_Country),]
dim(DPA)## [1] 366 22
##################################
# Listing all Predictors
# for the updated data
##################################
DPA.Predictors <- DPA[,!names(DPA) %in% c("COUNTRY","YEAR","LIFEXP")]
##################################
# Listing all numeric predictors
# for the updated data
##################################
DPA.Predictors.Numeric <- DPA.Predictors[,sapply(DPA.Predictors, is.numeric)]##################################
# Zero and Near-Zero Variance
##################################
##################################
# Identifying columns with low variance
###################################
DPA_LowVariance <- nearZeroVar(DPA,
freqCut = 80/20,
uniqueCut = 10,
saveMetrics= TRUE)
(DPA_LowVariance[DPA_LowVariance$nzv,])## freqRatio percentUnique zeroVar nzv
## YEAR 0 0.273224 TRUE TRUE
if ((nrow(DPA_LowVariance[DPA_LowVariance$nzv,]))==0){
print("No low variance predictors noted.")
} else {
print(paste0("Low variance observed for ",
(nrow(DPA_LowVariance[DPA_LowVariance$nzv,])),
" numeric variable(s) with First.Second.Mode.Ratio>4 and Unique.Count.Ratio<0.10."))
DPA_LowVarianceForRemoval <- (nrow(DPA_LowVariance[DPA_LowVariance$nzv,]))
print(paste0("Low variance can be resolved by removing ",
(nrow(DPA_LowVariance[DPA_LowVariance$nzv,])),
" numeric variable(s)."))
for (j in 1:DPA_LowVarianceForRemoval) {
DPA_LowVarianceRemovedVariable <- rownames(DPA_LowVariance[DPA_LowVariance$nzv,])[j]
print(paste0("Variable ",
j,
" for removal: ",
DPA_LowVarianceRemovedVariable))
}
DPA %>%
skim() %>%
dplyr::filter(skim_variable %in% rownames(DPA_LowVariance[DPA_LowVariance$nzv,]))
}## [1] "Low variance observed for 1 numeric variable(s) with First.Second.Mode.Ratio>4 and Unique.Count.Ratio<0.10."
## [1] "Low variance can be resolved by removing 1 numeric variable(s)."
## [1] "Variable 1 for removal: YEAR"
| Name | Piped data |
| Number of rows | 366 |
| Number of columns | 22 |
| _______________________ | |
| Column type frequency: | |
| factor | 1 |
| ________________________ | |
| Group variables | None |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| YEAR | 0 | 1 | FALSE | 1 | 201: 366 |
##################################
# Collinearity
##################################
##################################
# Visualizing pairwise correlation between predictors
##################################
DPA_CorrelationTest <- cor.mtest(DPA.Predictors.Numeric,
method = "pearson",
conf.level = .95)
corrplot(cor(DPA.Predictors.Numeric,
method = "pearson",
use="pairwise.complete.obs"),
method = "circle",
type = "upper",
order = "original",
tl.col = "black",
tl.cex = 0.75,
tl.srt = 90,
sig.level = 0.05,
p.mat = DPA_CorrelationTest$p,
insig = "blank")##################################
# Identifying the highly correlated variables
##################################
DPA_Correlation <- cor(DPA.Predictors.Numeric,
method = "pearson",
use="pairwise.complete.obs")
(DPA_HighlyCorrelatedCount <- sum(abs(DPA_Correlation[upper.tri(DPA_Correlation)]) > 0.75))## [1] 8
if (DPA_HighlyCorrelatedCount == 0) {
print("No highly correlated predictors noted.")
} else {
print(paste0("High correlation observed for ",
(DPA_HighlyCorrelatedCount),
" pairs of numeric variable(s) with Correlation.Coefficient>0.75."))
(DPA_HighlyCorrelatedPairs <- corr_cross(DPA.Predictors.Numeric,
max_pvalue = 0.05,
top = DPA_HighlyCorrelatedCount,
rm.na = TRUE,
grid = FALSE
))
}## [1] "High correlation observed for 8 pairs of numeric variable(s) with Correlation.Coefficient>0.75."
if (DPA_HighlyCorrelatedCount > 0) {
DPA_HighlyCorrelated <- findCorrelation(DPA_Correlation, cutoff = 0.75)
(DPA_HighlyCorrelatedForRemoval <- length(DPA_HighlyCorrelated))
print(paste0("High correlation can be resolved by removing ",
(DPA_HighlyCorrelatedForRemoval),
" numeric variable(s)."))
for (j in 1:DPA_HighlyCorrelatedForRemoval) {
DPA_HighlyCorrelatedRemovedVariable <- colnames(DPA.Predictors.Numeric)[DPA_HighlyCorrelated[j]]
print(paste0("Variable ",
j,
" for removal: ",
DPA_HighlyCorrelatedRemovedVariable))
}
}## [1] "High correlation can be resolved by removing 6 numeric variable(s)."
## [1] "Variable 1 for removal: INFMOR"
## [1] "Variable 2 for removal: CLTECH"
## [1] "Variable 3 for removal: URBPOP"
## [1] "Variable 4 for removal: DPTIMM"
## [1] "Variable 5 for removal: MEAIMM"
## [1] "Variable 6 for removal: GNI"
##################################
# Linear Dependencies
##################################
##################################
# Finding linear dependencies
##################################
DPA_LinearlyDependent <- findLinearCombos(DPA.Predictors.Numeric)
##################################
# Identifying the linearly dependent variables
##################################
DPA_LinearlyDependent <- findLinearCombos(DPA.Predictors.Numeric)
(DPA_LinearlyDependentCount <- length(DPA_LinearlyDependent$linearCombos))## [1] 0
if (DPA_LinearlyDependentCount == 0) {
print("No linearly dependent predictors noted.")
} else {
print(paste0("Linear dependency observed for ",
(DPA_LinearlyDependentCount),
" subset(s) of numeric variable(s)."))
for (i in 1:DPA_LinearlyDependentCount) {
DPA_LinearlyDependentSubset <- colnames(DPA.Predictors.Numeric)[DPA_LinearlyDependent$linearCombos[[i]]]
print(paste0("Linear dependent variable(s) for subset ",
i,
" include: ",
DPA_LinearlyDependentSubset))
}
}## [1] "No linearly dependent predictors noted."
##################################
# Identifying the linearly dependent variables for removal
##################################
if (DPA_LinearlyDependentCount > 0) {
DPA_LinearlyDependent <- findLinearCombos(DPA.Predictors.Numeric)
DPA_LinearlyDependentForRemoval <- length(DPA_LinearlyDependent$remove)
print(paste0("Linear dependency can be resolved by removing ",
(DPA_LinearlyDependentForRemoval),
" numeric variable(s)."))
for (j in 1:DPA_LinearlyDependentForRemoval) {
DPA_LinearlyDependentRemovedVariable <- colnames(DPA.Predictors.Numeric)[DPA_LinearlyDependent$remove[j]]
print(paste0("Variable ",
j,
" for removal: ",
DPA_LinearlyDependentRemovedVariable))
}
}##################################
# Shape Transformation
##################################
##################################
# Applying a Box-Cox transformation
##################################
DPA_BoxCox <- preProcess(DPA.Predictors.Numeric, method = c("BoxCox"))
DPA_BoxCoxTransformed <- predict(DPA_BoxCox, DPA.Predictors.Numeric)
for (i in 1:ncol(DPA_BoxCoxTransformed)) {
Median <- format(round(median(DPA_BoxCoxTransformed[,i],na.rm = TRUE),2), nsmall=2)
Mean <- format(round(mean(DPA_BoxCoxTransformed[,i],na.rm = TRUE),2), nsmall=2)
Skewness <- format(round(skewness(DPA_BoxCoxTransformed[,i],na.rm = TRUE),2), nsmall=2)
print(
ggplot(DPA_BoxCoxTransformed, aes(x=DPA_BoxCoxTransformed[,i])) +
geom_histogram(binwidth=1,color="black", fill="white") +
geom_vline(aes(xintercept=mean(DPA_BoxCoxTransformed[,i])),
color="blue", size=1) +
geom_vline(aes(xintercept=median(DPA_BoxCoxTransformed[,i])),
color="red", size=1) +
theme_bw() +
ylab("Count") +
xlab(names(DPA_BoxCoxTransformed)[i]) +
labs(title=names(DPA_BoxCoxTransformed)[i],
subtitle=paste0("Median = ", Median,
", Mean = ", Mean,
", Skewness = ", Skewness)))
}DPA_BoxCoxTransformed <- cbind(DPA_BoxCoxTransformed,DPA[,c("COUNTRY",
"YEAR",
"GENDER",
"LIFEXP")])##################################
# Creating the pre-modelling
# train set
##################################
PMA <- DPA_BoxCoxTransformed[,!names(DPA_BoxCoxTransformed) %in% c("YEAR",
"GNI",
"DPTIMM",
"MEAIMM",
"RURPOP",
"SANSER",
"RTIMOR")]
##################################
# Gathering descriptive statistics
##################################
(PMA_Skimmed <- skim(PMA))| Name | PMA |
| Number of rows | 366 |
| Number of columns | 15 |
| _______________________ | |
| Column type frequency: | |
| character | 1 |
| factor | 1 |
| numeric | 13 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| COUNTRY | 0 | 1 | 4 | 30 | 0 | 183 | 0 |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| GENDER | 0 | 1 | FALSE | 2 | Mal: 183, Fem: 183 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| UNEMPR | 0 | 1 | 2.12 | 1.21 | -2.05 | 1.42 | 2.00 | 2.90 | 5.26 | ▁▂▇▅▂ |
| INFMOR | 0 | 1 | 2.55 | 1.06 | 0.34 | 1.70 | 2.62 | 3.48 | 4.49 | ▅▆▇▇▆ |
| GDP | 0 | 1 | 3.89 | 2.26 | -1.67 | 2.55 | 3.80 | 5.55 | 10.05 | ▂▇▇▆▁ |
| CLTECH | 0 | 1 | 66.26 | 37.75 | 0.00 | 30.47 | 84.60 | 100.00 | 100.00 | ▃▁▁▂▇ |
| PERCAP | 0 | 1 | 1.77 | 1.41 | -1.48 | 0.65 | 1.80 | 2.85 | 4.73 | ▂▆▇▆▃ |
| TUBINC | 0 | 1 | 105.76 | 137.53 | 0.00 | 12.00 | 46.00 | 149.00 | 654.00 | ▇▂▁▁▁ |
| HEPIMM | 0 | 1 | 3877.73 | 1003.98 | 612.00 | 3339.73 | 4231.50 | 4704.00 | 4900.00 | ▁▁▁▃▇ |
| HOSBED | 0 | 1 | 0.77 | 0.85 | -1.61 | 0.15 | 0.84 | 1.40 | 2.62 | ▁▅▇▇▃ |
| TUBTRT | 0 | 1 | 77.90 | 17.27 | 0.00 | 73.25 | 83.00 | 88.00 | 100.00 | ▁▁▁▅▇ |
| URBPOP | 0 | 1 | 58.40 | 22.69 | 13.25 | 40.37 | 58.52 | 77.78 | 100.00 | ▅▆▇▇▅ |
| NCOMOR | 0 | 1 | 4.67 | 1.08 | 1.87 | 3.93 | 4.70 | 5.38 | 7.96 | ▂▅▇▃▁ |
| SUIRAT | 0 | 1 | 9.23 | 9.37 | 0.00 | 3.20 | 6.30 | 11.80 | 63.00 | ▇▂▁▁▁ |
| LIFEXP | 0 | 1 | 72.54 | 7.77 | 51.20 | 66.99 | 73.69 | 78.58 | 87.45 | ▁▃▆▇▅ |
##################################
# Loading dataset
##################################
PME <- PMA
PME.Numeric <- PME[,sapply(PME, is.numeric), drop = FALSE]
##################################
# Listing all Predictors
##################################
PME.Predictors <- PME[,!names(PME) %in% c("COUNTRY","LIFEXP")]
##################################
# Listing all numeric Predictors
##################################
PME.Predictors.Numeric <- PME.Predictors[,sapply(PME.Predictors, is.numeric), drop = FALSE]
ncol(PME.Predictors.Numeric)## [1] 12
##################################
# Listing all numeric Predictors
##################################
PME.Predictors.Factor <- PME.Predictors[,sapply(PME.Predictors, is.factor), drop = FALSE]
ncol(PME.Predictors.Factor)## [1] 1
##################################
# Formulating the scatter plot
##################################
featurePlot(x = PME.Predictors.Numeric,
y = PME$LIFEXP,
plot = "scatter",
type = c("p", "smooth"),
span = .5,
layout = c(4, 3))##################################
# Formulating the box plot
##################################
featurePlot(x = PME.Numeric,
y = PME$GENDER,
plot = "box",
scales = list(x = list(relation="free", rot = 90),
y = list(relation="free")),
adjust = 1.5,
layout = c(4, 4))##################################
# Evaluating model-independent
# feature importance metrics
##################################
##################################
# Obtaining the LOWESSPR pseudo-R-Squared
##################################
FE_LOWESSPR <- filterVarImp(x = PME.Numeric[,!names(PME.Numeric) %in% c("LIFEXP")],
y = PME$LIFEXP,
nonpara = TRUE)
##################################
# Formulating the summary table
##################################
FE_LOWESSPR_Summary <- FE_LOWESSPR
FE_LOWESSPR_Summary$Predictor <- rownames(FE_LOWESSPR)
names(FE_LOWESSPR_Summary)[1] <- "LOWESSPR"
FE_LOWESSPR_Summary$Metric <- rep("LOWESSPR",nrow(FE_LOWESSPR))
FE_LOWESSPR_Summary## LOWESSPR Predictor Metric
## UNEMPR 0.03820513 UNEMPR LOWESSPR
## INFMOR 0.82577754 INFMOR LOWESSPR
## GDP 0.25709988 GDP LOWESSPR
## CLTECH 0.58356246 CLTECH LOWESSPR
## PERCAP 0.62203760 PERCAP LOWESSPR
## TUBINC 0.51694851 TUBINC LOWESSPR
## HEPIMM 0.18580579 HEPIMM LOWESSPR
## HOSBED 0.34778666 HOSBED LOWESSPR
## TUBTRT 0.10269120 TUBTRT LOWESSPR
## URBPOP 0.35270735 URBPOP LOWESSPR
## NCOMOR 0.59824235 NCOMOR LOWESSPR
## SUIRAT 0.06391291 SUIRAT LOWESSPR
##################################
# Exploring predictor performance
# using LOWESS
##################################
dotplot(Predictor ~ LOWESSPR | Metric,
FE_LOWESSPR_Summary,
origin = 0,
type = c("p", "h"),
pch = 16,
cex = 2,
alpha = 0.45,
prepanel = function(x, y) {
list(ylim = levels(reorder(y, x)))
},
panel = function(x, y, ...) {
panel.dotplot(x, reorder(y, x), ...)
})##################################
# Obtaining the Pearson correlation coefficient
##################################
(FE_PCC <- abs(cor(PME.Numeric, method="pearson")[-13,13]))## UNEMPR INFMOR GDP CLTECH PERCAP TUBINC HEPIMM HOSBED
## 0.0173876 0.8799932 0.4607543 0.7531760 0.7852114 0.5904533 0.4310520 0.5632423
## TUBTRT URBPOP NCOMOR SUIRAT
## 0.3204547 0.5751200 0.7343442 0.1214945
##################################
# Formulating the summary table
##################################
FE_PCC_Summary <- data.frame(Predictor = names(PME.Numeric)[1:(ncol(PME.Numeric)-1)],
PCC = FE_PCC,
Metric = rep("PCC", length(FE_PCC)))
FE_PCC_Summary## Predictor PCC Metric
## UNEMPR UNEMPR 0.0173876 PCC
## INFMOR INFMOR 0.8799932 PCC
## GDP GDP 0.4607543 PCC
## CLTECH CLTECH 0.7531760 PCC
## PERCAP PERCAP 0.7852114 PCC
## TUBINC TUBINC 0.5904533 PCC
## HEPIMM HEPIMM 0.4310520 PCC
## HOSBED HOSBED 0.5632423 PCC
## TUBTRT TUBTRT 0.3204547 PCC
## URBPOP URBPOP 0.5751200 PCC
## NCOMOR NCOMOR 0.7343442 PCC
## SUIRAT SUIRAT 0.1214945 PCC
##################################
# Exploring predictor performance
# using PCC
##################################
dotplot(Predictor ~ PCC | Metric,
FE_PCC_Summary,
origin = 0,
type = c("p", "h"),
pch = 16,
cex = 2,
alpha = 0.45,
prepanel = function(x, y) {
list(ylim = levels(reorder(y, x)))
},
panel = function(x, y, ...) {
panel.dotplot(x, reorder(y, x), ...)
})##################################
# Obtaining the Spearman's rank correlation coefficient
##################################
(FE_SRCC <- abs(cor(PME.Numeric, method="spearman")[-13,13]))## UNEMPR INFMOR GDP CLTECH PERCAP TUBINC
## 0.007171824 0.891693321 0.502535269 0.784386965 0.798496072 0.716895672
## HEPIMM HOSBED TUBTRT URBPOP NCOMOR SUIRAT
## 0.378587047 0.555525640 0.335530704 0.602733552 0.791705740 0.116688271
##################################
# Formulating the summary table
##################################
FE_SRCC_Summary <- data.frame(Predictor = names(PME.Numeric)[1:(ncol(PME.Numeric)-1)],
SRCC = FE_SRCC,
Metric = rep("SRCC", length(FE_SRCC)))
FE_SRCC_Summary## Predictor SRCC Metric
## UNEMPR UNEMPR 0.007171824 SRCC
## INFMOR INFMOR 0.891693321 SRCC
## GDP GDP 0.502535269 SRCC
## CLTECH CLTECH 0.784386965 SRCC
## PERCAP PERCAP 0.798496072 SRCC
## TUBINC TUBINC 0.716895672 SRCC
## HEPIMM HEPIMM 0.378587047 SRCC
## HOSBED HOSBED 0.555525640 SRCC
## TUBTRT TUBTRT 0.335530704 SRCC
## URBPOP URBPOP 0.602733552 SRCC
## NCOMOR NCOMOR 0.791705740 SRCC
## SUIRAT SUIRAT 0.116688271 SRCC
##################################
# Exploring predictor performance
# using SRCC
##################################
dotplot(Predictor ~ SRCC | Metric,
FE_SRCC_Summary,
origin = 0,
type = c("p", "h"),
pch = 16,
cex = 2,
alpha = 0.45,
prepanel = function(x, y) {
list(ylim = levels(reorder(y, x)))
},
panel = function(x, y, ...) {
panel.dotplot(x, reorder(y, x), ...)
})##################################
# Obtaining the maximal information coefficient
##################################
FE_MIC <- mine(x = PME.Numeric[,!names(PME.Numeric) %in% c("LIFEXP")],
y = PME$LIFEXP)$MIC
##################################
# Formulating the summary table
##################################
FE_MIC_Summary <- data.frame(Predictor = names(PME.Numeric)[1:(ncol(PME.Numeric)-1)],
MIC = FE_MIC[,1],
Metric = rep("MIC", length(FE_MIC)))
FE_MIC_Summary## Predictor MIC Metric
## 1 UNEMPR 0.1884410 MIC
## 2 INFMOR 0.6990431 MIC
## 3 GDP 0.3199394 MIC
## 4 CLTECH 0.5129197 MIC
## 5 PERCAP 0.5532144 MIC
## 6 TUBINC 0.5141625 MIC
## 7 HEPIMM 0.2656190 MIC
## 8 HOSBED 0.3706681 MIC
## 9 TUBTRT 0.2392547 MIC
## 10 URBPOP 0.4107627 MIC
## 11 NCOMOR 0.6458233 MIC
## 12 SUIRAT 0.2314843 MIC
##################################
# Exploring predictor performance
# using MIC
##################################
dotplot(Predictor ~ MIC | Metric,
FE_MIC_Summary,
origin = 0,
type = c("p", "h"),
pch = 16,
cex = 2,
alpha = 0.45,
prepanel = function(x, y) {
list(ylim = levels(reorder(y, x)))
},
panel = function(x, y, ...) {
panel.dotplot(x, reorder(y, x), ...)
})##################################
# Obtaining the relief values
##################################
FE_RV <- attrEval(LIFEXP ~ .,
data = PME.Numeric,
estimator = "RReliefFequalK")
##################################
# Formulating the summary table
##################################
FE_RV_Summary <- data.frame(Predictor = names(FE_RV),
RV = FE_RV,
Metric = rep("RV", length(FE_RV)))
FE_RV_Summary## Predictor RV Metric
## UNEMPR UNEMPR 0.002897323 RV
## INFMOR INFMOR 0.092217298 RV
## GDP GDP -0.116882500 RV
## CLTECH CLTECH 0.036861936 RV
## PERCAP PERCAP -0.037386041 RV
## TUBINC TUBINC 0.066678316 RV
## HEPIMM HEPIMM -0.031239659 RV
## HOSBED HOSBED -0.052866119 RV
## TUBTRT TUBTRT -0.149965561 RV
## URBPOP URBPOP -0.105354115 RV
## NCOMOR NCOMOR 0.279415774 RV
## SUIRAT SUIRAT 0.110919970 RV
##################################
# Exploring predictor performance
##################################
dotplot(Predictor ~ RV | Metric,
FE_RV_Summary,
origin = 0,
type = c("p", "h"),
pch = 16,
cex = 2,
alpha = 0.45,
prepanel = function(x, y) {
list(ylim = levels(reorder(y, x)))
},
panel = function(x, y, ...) {
panel.dotplot(x, reorder(y, x), ...)
})##################################
# Preparing the dataset for
# model development and test
##################################
set.seed(12345678)
trainIndex <- createDataPartition(PME$LIFEXP,
p = 0.8,
list = FALSE,
times = 1)
##################################
# Formulating the model development data
##################################
MD <- PME[ trainIndex,]
##################################
# Formulating the model test data
##################################
MT <- PME[-trainIndex,]
##################################
# Preparing the dataset for
# model development
##################################
MD <- MD[,c("GENDER","INFMOR","PERCAP","CLTECH","NCOMOR","LIFEXP")]
MD.Model.Predictors <- MD[,c("GENDER","INFMOR","PERCAP","CLTECH","NCOMOR")]
##################################
# Preparing the dataset for
# model test
##################################
MT <- MT[,c("GENDER","INFMOR","PERCAP","CLTECH","NCOMOR","LIFEXP")]
MT.Model.Predictors <- MT[,c("GENDER","INFMOR","PERCAP","CLTECH","NCOMOR")]
##################################
# Creating consistent fold assignments
# for the 10-Fold Cross Validation process
##################################
set.seed(12345678)
KFold_Indices <- createFolds(MD$LIFEXP,
k = 10,
returnTrain=TRUE)
KFold_Control <- trainControl(method="cv",
index=KFold_Indices)
##################################
# Defining the model hyperparameter values
# for the GBM model
##################################
GBM_Grid = expand.grid(n.trees = c(100, 200, 300),
interaction.depth = c(1, 3, 5),
shrinkage = c(0.10,0.05,0.01),
n.minobsinnode = c(15,10,5))
##################################
# Running the GBM model
# by setting the caret method to 'gbm'
##################################
set.seed(12345678)
GBM_Tune <- train(x = MD.Model.Predictors,
y = MD$LIFEXP,
method = "gbm",
tuneGrid = GBM_Grid,
trControl = KFold_Control)## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.1227 nan 0.0100 0.7693
## 2 61.3337 nan 0.0100 0.6983
## 3 60.6061 nan 0.0100 0.6633
## 4 59.7836 nan 0.0100 0.7341
## 5 59.0098 nan 0.0100 0.7139
## 6 58.2788 nan 0.0100 0.6988
## 7 57.5535 nan 0.0100 0.7128
## 8 56.9286 nan 0.0100 0.7258
## 9 56.1944 nan 0.0100 0.6810
## 10 55.5511 nan 0.0100 0.7079
## 20 49.5540 nan 0.0100 0.5830
## 40 39.9875 nan 0.0100 0.3742
## 60 32.7024 nan 0.0100 0.2837
## 80 27.1565 nan 0.0100 0.2605
## 100 22.8699 nan 0.0100 0.1831
## 120 19.6043 nan 0.0100 0.1340
## 140 16.9933 nan 0.0100 0.1047
## 160 14.8505 nan 0.0100 0.0993
## 180 13.2168 nan 0.0100 0.0699
## 200 11.8566 nan 0.0100 0.0406
## 220 10.7300 nan 0.0100 0.0462
## 240 9.7666 nan 0.0100 0.0397
## 260 8.9376 nan 0.0100 0.0338
## 280 8.2565 nan 0.0100 0.0243
## 300 7.6740 nan 0.0100 0.0186
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.0988 nan 0.0100 0.7940
## 2 61.3156 nan 0.0100 0.7742
## 3 60.5943 nan 0.0100 0.7824
## 4 59.7818 nan 0.0100 0.7272
## 5 59.0743 nan 0.0100 0.7264
## 6 58.3737 nan 0.0100 0.6166
## 7 57.6752 nan 0.0100 0.6979
## 8 56.9330 nan 0.0100 0.7227
## 9 56.2539 nan 0.0100 0.7127
## 10 55.5820 nan 0.0100 0.6156
## 20 49.4017 nan 0.0100 0.5509
## 40 39.8044 nan 0.0100 0.3929
## 60 32.6954 nan 0.0100 0.2998
## 80 27.0214 nan 0.0100 0.2255
## 100 22.8076 nan 0.0100 0.1919
## 120 19.5829 nan 0.0100 0.1334
## 140 16.8504 nan 0.0100 0.0961
## 160 14.7563 nan 0.0100 0.0761
## 180 13.0689 nan 0.0100 0.0810
## 200 11.6840 nan 0.0100 0.0492
## 220 10.5358 nan 0.0100 0.0446
## 240 9.5983 nan 0.0100 0.0374
## 260 8.8306 nan 0.0100 0.0197
## 280 8.1503 nan 0.0100 0.0261
## 300 7.6018 nan 0.0100 0.0186
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.1245 nan 0.0100 0.8097
## 2 61.2860 nan 0.0100 0.8222
## 3 60.5177 nan 0.0100 0.7625
## 4 59.8337 nan 0.0100 0.7230
## 5 59.1519 nan 0.0100 0.7023
## 6 58.3911 nan 0.0100 0.6609
## 7 57.7117 nan 0.0100 0.7349
## 8 56.9769 nan 0.0100 0.7364
## 9 56.3176 nan 0.0100 0.6742
## 10 55.5860 nan 0.0100 0.6828
## 20 49.3356 nan 0.0100 0.5617
## 40 39.8248 nan 0.0100 0.4355
## 60 32.5515 nan 0.0100 0.3474
## 80 27.0736 nan 0.0100 0.2165
## 100 22.8876 nan 0.0100 0.1807
## 120 19.5812 nan 0.0100 0.1292
## 140 17.0003 nan 0.0100 0.0993
## 160 14.9433 nan 0.0100 0.0780
## 180 13.2105 nan 0.0100 0.0633
## 200 11.7907 nan 0.0100 0.0527
## 220 10.6580 nan 0.0100 0.0298
## 240 9.7092 nan 0.0100 0.0303
## 260 8.9433 nan 0.0100 0.0293
## 280 8.2702 nan 0.0100 0.0224
## 300 7.6993 nan 0.0100 0.0110
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.8484 nan 0.0100 1.0058
## 2 60.8409 nan 0.0100 0.9750
## 3 59.8266 nan 0.0100 1.0107
## 4 58.8552 nan 0.0100 0.9944
## 5 57.8969 nan 0.0100 0.8557
## 6 56.9014 nan 0.0100 1.0323
## 7 56.0024 nan 0.0100 0.8546
## 8 55.1274 nan 0.0100 0.9597
## 9 54.1694 nan 0.0100 0.9515
## 10 53.3205 nan 0.0100 0.8718
## 20 45.4893 nan 0.0100 0.7193
## 40 33.3136 nan 0.0100 0.4897
## 60 25.1491 nan 0.0100 0.2779
## 80 19.1531 nan 0.0100 0.2291
## 100 15.0084 nan 0.0100 0.1369
## 120 12.0067 nan 0.0100 0.1216
## 140 9.8430 nan 0.0100 0.0754
## 160 8.2437 nan 0.0100 0.0535
## 180 7.1110 nan 0.0100 0.0512
## 200 6.2282 nan 0.0100 0.0366
## 220 5.6014 nan 0.0100 0.0207
## 240 5.1055 nan 0.0100 0.0170
## 260 4.7446 nan 0.0100 0.0102
## 280 4.4574 nan 0.0100 0.0061
## 300 4.2377 nan 0.0100 -0.0009
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.8964 nan 0.0100 0.9073
## 2 60.9011 nan 0.0100 0.9375
## 3 59.9218 nan 0.0100 0.8415
## 4 58.9196 nan 0.0100 1.0333
## 5 57.9778 nan 0.0100 0.9587
## 6 57.0334 nan 0.0100 0.9181
## 7 56.1133 nan 0.0100 0.9913
## 8 55.2123 nan 0.0100 0.8484
## 9 54.3694 nan 0.0100 0.7972
## 10 53.4861 nan 0.0100 0.8989
## 20 45.4135 nan 0.0100 0.7464
## 40 33.3772 nan 0.0100 0.4940
## 60 25.0348 nan 0.0100 0.3264
## 80 19.1679 nan 0.0100 0.2903
## 100 15.0754 nan 0.0100 0.2075
## 120 12.1305 nan 0.0100 0.1155
## 140 9.9898 nan 0.0100 0.0771
## 160 8.4204 nan 0.0100 0.0615
## 180 7.2428 nan 0.0100 0.0401
## 200 6.3844 nan 0.0100 0.0321
## 220 5.7542 nan 0.0100 0.0188
## 240 5.2573 nan 0.0100 0.0117
## 260 4.9011 nan 0.0100 0.0102
## 280 4.6129 nan 0.0100 0.0003
## 300 4.4132 nan 0.0100 0.0027
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.8232 nan 0.0100 1.0853
## 2 60.7615 nan 0.0100 1.0726
## 3 59.7809 nan 0.0100 1.0614
## 4 58.7383 nan 0.0100 0.9429
## 5 57.7943 nan 0.0100 1.0443
## 6 56.8382 nan 0.0100 0.9865
## 7 55.9356 nan 0.0100 0.9874
## 8 55.0589 nan 0.0100 0.8711
## 9 54.1475 nan 0.0100 0.8512
## 10 53.2693 nan 0.0100 0.8231
## 20 45.5233 nan 0.0100 0.7284
## 40 33.3373 nan 0.0100 0.5334
## 60 25.0338 nan 0.0100 0.3390
## 80 19.1644 nan 0.0100 0.2640
## 100 15.0638 nan 0.0100 0.1606
## 120 12.1221 nan 0.0100 0.1083
## 140 10.0718 nan 0.0100 0.0768
## 160 8.5848 nan 0.0100 0.0642
## 180 7.4393 nan 0.0100 0.0463
## 200 6.5853 nan 0.0100 0.0221
## 220 5.9490 nan 0.0100 0.0210
## 240 5.4649 nan 0.0100 0.0117
## 260 5.0887 nan 0.0100 0.0037
## 280 4.8247 nan 0.0100 -0.0003
## 300 4.6158 nan 0.0100 0.0061
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.8567 nan 0.0100 1.0966
## 2 60.7933 nan 0.0100 1.1470
## 3 59.7505 nan 0.0100 1.1401
## 4 58.7434 nan 0.0100 0.9251
## 5 57.7391 nan 0.0100 1.0180
## 6 56.7267 nan 0.0100 1.0002
## 7 55.7543 nan 0.0100 0.9079
## 8 54.7552 nan 0.0100 0.9434
## 9 53.8335 nan 0.0100 0.9930
## 10 52.9253 nan 0.0100 0.8661
## 20 44.5694 nan 0.0100 0.6188
## 40 32.0461 nan 0.0100 0.5462
## 60 23.4480 nan 0.0100 0.3912
## 80 17.4106 nan 0.0100 0.2258
## 100 13.2948 nan 0.0100 0.1607
## 120 10.4197 nan 0.0100 0.0962
## 140 8.3610 nan 0.0100 0.0711
## 160 6.9310 nan 0.0100 0.0478
## 180 5.9367 nan 0.0100 0.0349
## 200 5.1993 nan 0.0100 0.0156
## 220 4.6451 nan 0.0100 0.0128
## 240 4.2325 nan 0.0100 0.0075
## 260 3.9176 nan 0.0100 0.0065
## 280 3.6660 nan 0.0100 0.0060
## 300 3.4646 nan 0.0100 0.0044
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.8350 nan 0.0100 1.0879
## 2 60.7413 nan 0.0100 1.0083
## 3 59.6438 nan 0.0100 1.0774
## 4 58.5917 nan 0.0100 0.9423
## 5 57.5165 nan 0.0100 0.9410
## 6 56.5438 nan 0.0100 0.9834
## 7 55.5377 nan 0.0100 0.9295
## 8 54.5458 nan 0.0100 0.8482
## 9 53.5609 nan 0.0100 0.8309
## 10 52.6312 nan 0.0100 0.8911
## 20 44.3743 nan 0.0100 0.7673
## 40 31.8672 nan 0.0100 0.4832
## 60 23.2646 nan 0.0100 0.3351
## 80 17.3926 nan 0.0100 0.2176
## 100 13.2413 nan 0.0100 0.1499
## 120 10.4238 nan 0.0100 0.1254
## 140 8.4523 nan 0.0100 0.0771
## 160 7.0427 nan 0.0100 0.0488
## 180 6.0645 nan 0.0100 0.0377
## 200 5.3543 nan 0.0100 0.0211
## 220 4.8252 nan 0.0100 0.0136
## 240 4.4383 nan 0.0100 0.0061
## 260 4.1290 nan 0.0100 0.0058
## 280 3.9063 nan 0.0100 0.0012
## 300 3.7428 nan 0.0100 0.0009
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.7795 nan 0.0100 1.1843
## 2 60.7281 nan 0.0100 1.0092
## 3 59.6082 nan 0.0100 1.0506
## 4 58.5483 nan 0.0100 1.0928
## 5 57.5230 nan 0.0100 1.0513
## 6 56.5212 nan 0.0100 0.9589
## 7 55.5274 nan 0.0100 1.0002
## 8 54.6074 nan 0.0100 0.9283
## 9 53.6685 nan 0.0100 1.0439
## 10 52.7635 nan 0.0100 0.9825
## 20 44.5357 nan 0.0100 0.7147
## 40 32.1380 nan 0.0100 0.4751
## 60 23.5894 nan 0.0100 0.3759
## 80 17.6773 nan 0.0100 0.2295
## 100 13.6115 nan 0.0100 0.1471
## 120 10.7324 nan 0.0100 0.0983
## 140 8.7455 nan 0.0100 0.0588
## 160 7.3378 nan 0.0100 0.0603
## 180 6.3314 nan 0.0100 0.0440
## 200 5.6190 nan 0.0100 0.0307
## 220 5.1231 nan 0.0100 0.0156
## 240 4.7532 nan 0.0100 0.0019
## 260 4.4648 nan 0.0100 0.0116
## 280 4.2383 nan 0.0100 0.0045
## 300 4.0646 nan 0.0100 -0.0003
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.0285 nan 0.0500 3.7847
## 2 55.2280 nan 0.0500 3.4680
## 3 52.0630 nan 0.0500 3.0705
## 4 49.0940 nan 0.0500 2.7595
## 5 46.3520 nan 0.0500 2.7705
## 6 43.8739 nan 0.0500 2.3552
## 7 41.5192 nan 0.0500 2.3073
## 8 39.4099 nan 0.0500 2.0434
## 9 37.5930 nan 0.0500 1.7674
## 10 35.7126 nan 0.0500 1.8581
## 20 22.3387 nan 0.0500 0.9188
## 40 11.6088 nan 0.0500 0.3617
## 60 7.4938 nan 0.0500 0.0612
## 80 5.7633 nan 0.0500 0.0581
## 100 4.9643 nan 0.0500 0.0286
## 120 4.5650 nan 0.0500 0.0075
## 140 4.3793 nan 0.0500 -0.0163
## 160 4.2708 nan 0.0500 -0.0097
## 180 4.1721 nan 0.0500 -0.0002
## 200 4.1168 nan 0.0500 -0.0102
## 220 4.0538 nan 0.0500 -0.0184
## 240 4.0048 nan 0.0500 -0.0171
## 260 3.9464 nan 0.0500 -0.0345
## 280 3.9020 nan 0.0500 -0.0108
## 300 3.8507 nan 0.0500 -0.0057
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.2877 nan 0.0500 3.7427
## 2 55.8983 nan 0.0500 3.4934
## 3 52.5998 nan 0.0500 3.1757
## 4 49.7127 nan 0.0500 2.8915
## 5 46.9318 nan 0.0500 2.6198
## 6 44.5185 nan 0.0500 2.1359
## 7 41.9770 nan 0.0500 2.3058
## 8 39.8183 nan 0.0500 2.3300
## 9 37.9860 nan 0.0500 1.7638
## 10 35.9557 nan 0.0500 1.8572
## 20 22.8119 nan 0.0500 0.8712
## 40 11.5042 nan 0.0500 0.3279
## 60 7.5010 nan 0.0500 0.0908
## 80 5.8261 nan 0.0500 0.0300
## 100 5.0749 nan 0.0500 -0.0070
## 120 4.6814 nan 0.0500 -0.0055
## 140 4.5318 nan 0.0500 -0.0097
## 160 4.3929 nan 0.0500 -0.0004
## 180 4.3060 nan 0.0500 -0.0035
## 200 4.2341 nan 0.0500 -0.0337
## 220 4.1675 nan 0.0500 -0.0103
## 240 4.1152 nan 0.0500 -0.0162
## 260 4.0526 nan 0.0500 -0.0057
## 280 4.0087 nan 0.0500 -0.0063
## 300 3.9599 nan 0.0500 -0.0075
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.9434 nan 0.0500 4.1606
## 2 55.6102 nan 0.0500 3.6048
## 3 52.3485 nan 0.0500 3.2391
## 4 49.4775 nan 0.0500 3.1181
## 5 46.8498 nan 0.0500 2.5629
## 6 44.3032 nan 0.0500 2.3166
## 7 41.9600 nan 0.0500 1.9276
## 8 39.6646 nan 0.0500 2.2852
## 9 37.5780 nan 0.0500 2.0908
## 10 35.7240 nan 0.0500 1.7903
## 20 22.5620 nan 0.0500 0.9948
## 40 11.7572 nan 0.0500 0.2593
## 60 7.6888 nan 0.0500 0.1064
## 80 6.0366 nan 0.0500 0.0430
## 100 5.2608 nan 0.0500 -0.0022
## 120 4.8910 nan 0.0500 -0.0164
## 140 4.7140 nan 0.0500 -0.0105
## 160 4.6063 nan 0.0500 -0.0039
## 180 4.5044 nan 0.0500 -0.0144
## 200 4.4191 nan 0.0500 -0.0043
## 220 4.3510 nan 0.0500 -0.0239
## 240 4.2914 nan 0.0500 -0.0222
## 260 4.2361 nan 0.0500 -0.0179
## 280 4.1922 nan 0.0500 -0.0030
## 300 4.1440 nan 0.0500 -0.0071
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.6101 nan 0.0500 4.6628
## 2 53.2366 nan 0.0500 4.5087
## 3 49.2002 nan 0.0500 4.5266
## 4 45.3188 nan 0.0500 3.6167
## 5 42.0193 nan 0.0500 3.6309
## 6 39.0588 nan 0.0500 3.1434
## 7 35.9386 nan 0.0500 2.6117
## 8 33.3803 nan 0.0500 2.4874
## 9 30.7484 nan 0.0500 2.6640
## 10 28.6123 nan 0.0500 1.9950
## 20 14.7083 nan 0.0500 0.8919
## 40 6.2055 nan 0.0500 0.1508
## 60 4.2665 nan 0.0500 0.0072
## 80 3.5928 nan 0.0500 -0.0036
## 100 3.3012 nan 0.0500 -0.0211
## 120 3.0554 nan 0.0500 -0.0101
## 140 2.8697 nan 0.0500 -0.0080
## 160 2.6751 nan 0.0500 0.0018
## 180 2.5134 nan 0.0500 -0.0095
## 200 2.3650 nan 0.0500 -0.0143
## 220 2.2550 nan 0.0500 -0.0048
## 240 2.1278 nan 0.0500 -0.0187
## 260 2.0159 nan 0.0500 -0.0129
## 280 1.9232 nan 0.0500 -0.0215
## 300 1.8330 nan 0.0500 -0.0050
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.7043 nan 0.0500 5.4844
## 2 53.0352 nan 0.0500 4.7683
## 3 48.7327 nan 0.0500 4.1390
## 4 44.8769 nan 0.0500 3.9403
## 5 41.5176 nan 0.0500 3.4863
## 6 38.3935 nan 0.0500 2.6453
## 7 35.6388 nan 0.0500 2.7774
## 8 33.0377 nan 0.0500 2.6318
## 9 30.5692 nan 0.0500 2.2881
## 10 28.3643 nan 0.0500 2.0766
## 20 14.6763 nan 0.0500 0.8302
## 40 6.1216 nan 0.0500 0.1411
## 60 4.2930 nan 0.0500 0.0187
## 80 3.7666 nan 0.0500 -0.0051
## 100 3.4912 nan 0.0500 -0.0239
## 120 3.2857 nan 0.0500 -0.0090
## 140 3.1191 nan 0.0500 -0.0111
## 160 2.9533 nan 0.0500 -0.0189
## 180 2.8280 nan 0.0500 -0.0113
## 200 2.7362 nan 0.0500 -0.0128
## 220 2.6297 nan 0.0500 -0.0116
## 240 2.5261 nan 0.0500 -0.0172
## 260 2.4411 nan 0.0500 -0.0207
## 280 2.3411 nan 0.0500 -0.0067
## 300 2.2573 nan 0.0500 -0.0244
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.5689 nan 0.0500 4.5040
## 2 52.8280 nan 0.0500 4.4066
## 3 48.7064 nan 0.0500 4.4652
## 4 44.8798 nan 0.0500 3.5547
## 5 41.3601 nan 0.0500 3.4924
## 6 38.1744 nan 0.0500 3.1494
## 7 35.3853 nan 0.0500 2.6172
## 8 32.7987 nan 0.0500 2.4956
## 9 30.4805 nan 0.0500 2.1042
## 10 28.3960 nan 0.0500 2.0829
## 20 14.7823 nan 0.0500 0.7540
## 40 6.3443 nan 0.0500 0.1509
## 60 4.5748 nan 0.0500 0.0215
## 80 4.0581 nan 0.0500 -0.0097
## 100 3.7625 nan 0.0500 -0.0099
## 120 3.5690 nan 0.0500 -0.0071
## 140 3.4191 nan 0.0500 -0.0278
## 160 3.2920 nan 0.0500 -0.0087
## 180 3.1607 nan 0.0500 -0.0149
## 200 3.0388 nan 0.0500 -0.0067
## 220 2.9503 nan 0.0500 -0.0075
## 240 2.8705 nan 0.0500 -0.0194
## 260 2.7842 nan 0.0500 -0.0290
## 280 2.7023 nan 0.0500 -0.0095
## 300 2.6160 nan 0.0500 -0.0091
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.6070 nan 0.0500 5.5350
## 2 52.9644 nan 0.0500 4.4937
## 3 48.6983 nan 0.0500 4.6190
## 4 44.3573 nan 0.0500 3.6627
## 5 40.5895 nan 0.0500 4.0002
## 6 37.0560 nan 0.0500 3.2714
## 7 34.1248 nan 0.0500 3.2003
## 8 31.3187 nan 0.0500 2.5264
## 9 28.9119 nan 0.0500 2.4922
## 10 26.6396 nan 0.0500 2.1793
## 20 12.8245 nan 0.0500 0.8007
## 40 5.0838 nan 0.0500 0.0937
## 60 3.4329 nan 0.0500 0.0155
## 80 2.9072 nan 0.0500 -0.0065
## 100 2.5530 nan 0.0500 -0.0492
## 120 2.3286 nan 0.0500 -0.0274
## 140 2.1165 nan 0.0500 -0.0093
## 160 1.9288 nan 0.0500 -0.0171
## 180 1.7452 nan 0.0500 -0.0105
## 200 1.6083 nan 0.0500 -0.0147
## 220 1.4810 nan 0.0500 -0.0068
## 240 1.3548 nan 0.0500 -0.0130
## 260 1.2571 nan 0.0500 -0.0107
## 280 1.1736 nan 0.0500 -0.0046
## 300 1.0998 nan 0.0500 -0.0045
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.6074 nan 0.0500 5.0928
## 2 52.6480 nan 0.0500 4.9163
## 3 48.2102 nan 0.0500 3.6347
## 4 44.2609 nan 0.0500 4.1666
## 5 40.3370 nan 0.0500 3.6530
## 6 37.0098 nan 0.0500 3.0094
## 7 34.0725 nan 0.0500 3.0988
## 8 31.3034 nan 0.0500 2.5954
## 9 28.7670 nan 0.0500 2.3194
## 10 26.5441 nan 0.0500 2.1188
## 20 12.7643 nan 0.0500 0.7245
## 40 5.2802 nan 0.0500 0.0658
## 60 3.7291 nan 0.0500 0.0021
## 80 3.2486 nan 0.0500 -0.0042
## 100 2.9757 nan 0.0500 -0.0204
## 120 2.7167 nan 0.0500 -0.0158
## 140 2.5299 nan 0.0500 -0.0166
## 160 2.3685 nan 0.0500 -0.0202
## 180 2.2391 nan 0.0500 -0.0166
## 200 2.1204 nan 0.0500 -0.0182
## 220 1.9995 nan 0.0500 -0.0183
## 240 1.8869 nan 0.0500 -0.0239
## 260 1.7872 nan 0.0500 -0.0191
## 280 1.7016 nan 0.0500 -0.0199
## 300 1.6129 nan 0.0500 -0.0161
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.3179 nan 0.0500 5.0482
## 2 52.6618 nan 0.0500 4.7157
## 3 48.3907 nan 0.0500 4.2879
## 4 44.5964 nan 0.0500 4.0063
## 5 41.2366 nan 0.0500 3.9352
## 6 37.9516 nan 0.0500 3.0408
## 7 34.9277 nan 0.0500 2.7338
## 8 32.2526 nan 0.0500 2.7502
## 9 29.7876 nan 0.0500 2.3907
## 10 27.4730 nan 0.0500 2.3297
## 20 13.5299 nan 0.0500 0.7158
## 40 5.6291 nan 0.0500 0.0923
## 60 4.2428 nan 0.0500 0.0166
## 80 3.7190 nan 0.0500 -0.0266
## 100 3.4311 nan 0.0500 -0.0135
## 120 3.2119 nan 0.0500 -0.0191
## 140 3.0013 nan 0.0500 -0.0179
## 160 2.8499 nan 0.0500 -0.0149
## 180 2.6918 nan 0.0500 -0.0134
## 200 2.5854 nan 0.0500 -0.0091
## 220 2.4537 nan 0.0500 -0.0169
## 240 2.3694 nan 0.0500 -0.0126
## 260 2.2707 nan 0.0500 -0.0095
## 280 2.1794 nan 0.0500 -0.0179
## 300 2.0977 nan 0.0500 -0.0161
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.0555 nan 0.1000 7.6138
## 2 49.4053 nan 0.1000 6.6276
## 3 44.3756 nan 0.1000 4.9929
## 4 39.5129 nan 0.1000 4.2009
## 5 35.6855 nan 0.1000 3.3459
## 6 31.8681 nan 0.1000 3.3389
## 7 29.1277 nan 0.1000 2.3886
## 8 26.5008 nan 0.1000 2.8212
## 9 24.3133 nan 0.1000 2.3781
## 10 22.4860 nan 0.1000 1.9166
## 20 11.6166 nan 0.1000 0.4115
## 40 5.7238 nan 0.1000 0.0428
## 60 4.5947 nan 0.1000 -0.0107
## 80 4.3573 nan 0.1000 -0.0302
## 100 4.1669 nan 0.1000 -0.0328
## 120 4.0450 nan 0.1000 -0.0275
## 140 3.9471 nan 0.1000 -0.0277
## 160 3.8459 nan 0.1000 -0.0134
## 180 3.7614 nan 0.1000 -0.0474
## 200 3.6922 nan 0.1000 -0.0321
## 220 3.6113 nan 0.1000 -0.0294
## 240 3.5395 nan 0.1000 -0.0268
## 260 3.4978 nan 0.1000 -0.0151
## 280 3.4414 nan 0.1000 -0.0290
## 300 3.3800 nan 0.1000 -0.0228
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.8302 nan 0.1000 7.7743
## 2 49.2898 nan 0.1000 6.5261
## 3 43.6756 nan 0.1000 5.5281
## 4 38.9161 nan 0.1000 4.2792
## 5 35.2476 nan 0.1000 3.5644
## 6 31.9980 nan 0.1000 3.3527
## 7 29.1925 nan 0.1000 2.9808
## 8 26.6720 nan 0.1000 2.2059
## 9 24.4612 nan 0.1000 2.3958
## 10 22.3754 nan 0.1000 1.9165
## 20 11.4983 nan 0.1000 0.5774
## 40 5.7692 nan 0.1000 0.0864
## 60 4.6802 nan 0.1000 -0.0218
## 80 4.3781 nan 0.1000 -0.0262
## 100 4.2141 nan 0.1000 -0.0211
## 120 4.0957 nan 0.1000 -0.0234
## 140 4.0209 nan 0.1000 -0.0118
## 160 3.9366 nan 0.1000 -0.0429
## 180 3.8693 nan 0.1000 -0.0056
## 200 3.8009 nan 0.1000 -0.0598
## 220 3.7237 nan 0.1000 -0.0110
## 240 3.6610 nan 0.1000 -0.0210
## 260 3.5977 nan 0.1000 -0.0242
## 280 3.5416 nan 0.1000 -0.0202
## 300 3.4865 nan 0.1000 -0.0263
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.1560 nan 0.1000 7.5652
## 2 48.7478 nan 0.1000 7.1845
## 3 44.1969 nan 0.1000 5.0202
## 4 39.5508 nan 0.1000 4.1347
## 5 35.6722 nan 0.1000 4.4838
## 6 32.5038 nan 0.1000 3.4300
## 7 29.6769 nan 0.1000 2.8936
## 8 27.1966 nan 0.1000 2.4818
## 9 24.8045 nan 0.1000 2.2810
## 10 23.0550 nan 0.1000 1.4913
## 20 11.6762 nan 0.1000 0.6798
## 40 6.1148 nan 0.1000 0.0010
## 60 5.0670 nan 0.1000 0.0180
## 80 4.8137 nan 0.1000 -0.0086
## 100 4.6319 nan 0.1000 -0.0072
## 120 4.4658 nan 0.1000 -0.0142
## 140 4.3333 nan 0.1000 -0.0153
## 160 4.2019 nan 0.1000 -0.0058
## 180 4.1027 nan 0.1000 -0.0075
## 200 4.0159 nan 0.1000 -0.0067
## 220 3.9518 nan 0.1000 -0.0037
## 240 3.8675 nan 0.1000 -0.0124
## 260 3.8004 nan 0.1000 -0.0308
## 280 3.7408 nan 0.1000 -0.0082
## 300 3.6883 nan 0.1000 -0.0310
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.0963 nan 0.1000 9.8866
## 2 45.0318 nan 0.1000 7.6146
## 3 38.3788 nan 0.1000 6.0393
## 4 32.5579 nan 0.1000 5.8196
## 5 28.3663 nan 0.1000 4.4236
## 6 24.5766 nan 0.1000 3.4699
## 7 21.2377 nan 0.1000 2.9415
## 8 18.5014 nan 0.1000 2.6313
## 9 16.2677 nan 0.1000 2.1613
## 10 14.5832 nan 0.1000 1.6213
## 20 6.1299 nan 0.1000 0.2781
## 40 3.6978 nan 0.1000 -0.0354
## 60 3.1180 nan 0.1000 -0.0488
## 80 2.7482 nan 0.1000 -0.0412
## 100 2.4495 nan 0.1000 -0.0057
## 120 2.2052 nan 0.1000 -0.0500
## 140 2.0275 nan 0.1000 -0.0195
## 160 1.8484 nan 0.1000 -0.0145
## 180 1.6781 nan 0.1000 -0.0089
## 200 1.5636 nan 0.1000 -0.0083
## 220 1.4625 nan 0.1000 -0.0095
## 240 1.3620 nan 0.1000 -0.0195
## 260 1.2561 nan 0.1000 -0.0181
## 280 1.1577 nan 0.1000 -0.0176
## 300 1.0865 nan 0.1000 -0.0142
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.4982 nan 0.1000 9.9704
## 2 45.4958 nan 0.1000 8.1663
## 3 38.8439 nan 0.1000 6.8676
## 4 33.1167 nan 0.1000 5.0847
## 5 28.6498 nan 0.1000 5.0926
## 6 24.9079 nan 0.1000 3.6444
## 7 21.5416 nan 0.1000 3.4117
## 8 18.8026 nan 0.1000 2.5982
## 9 16.4677 nan 0.1000 2.2438
## 10 14.4618 nan 0.1000 1.6878
## 20 6.2608 nan 0.1000 0.1583
## 40 3.9947 nan 0.1000 0.0048
## 60 3.4532 nan 0.1000 -0.0695
## 80 3.0753 nan 0.1000 -0.0239
## 100 2.8015 nan 0.1000 -0.0292
## 120 2.6091 nan 0.1000 -0.0193
## 140 2.4383 nan 0.1000 -0.0359
## 160 2.2716 nan 0.1000 -0.0380
## 180 2.1292 nan 0.1000 -0.0148
## 200 2.0087 nan 0.1000 -0.0186
## 220 1.8971 nan 0.1000 -0.0416
## 240 1.7802 nan 0.1000 -0.0417
## 260 1.6889 nan 0.1000 -0.0367
## 280 1.6110 nan 0.1000 -0.0245
## 300 1.5485 nan 0.1000 -0.0115
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.9706 nan 0.1000 8.4499
## 2 44.8951 nan 0.1000 7.8972
## 3 38.5061 nan 0.1000 6.6447
## 4 32.9478 nan 0.1000 5.5577
## 5 28.5628 nan 0.1000 4.6357
## 6 24.6281 nan 0.1000 4.3533
## 7 21.4555 nan 0.1000 2.9754
## 8 18.7108 nan 0.1000 2.6593
## 9 16.3322 nan 0.1000 2.0587
## 10 14.7445 nan 0.1000 1.3797
## 20 6.5646 nan 0.1000 0.2890
## 40 4.2031 nan 0.1000 0.0166
## 60 3.7148 nan 0.1000 -0.0206
## 80 3.3714 nan 0.1000 -0.0672
## 100 3.1552 nan 0.1000 -0.0106
## 120 2.9588 nan 0.1000 -0.0278
## 140 2.7862 nan 0.1000 -0.0357
## 160 2.6385 nan 0.1000 -0.0402
## 180 2.4985 nan 0.1000 -0.0109
## 200 2.3784 nan 0.1000 -0.0173
## 220 2.2683 nan 0.1000 -0.0245
## 240 2.1426 nan 0.1000 -0.0382
## 260 2.0659 nan 0.1000 -0.0299
## 280 1.9689 nan 0.1000 -0.0278
## 300 1.8860 nan 0.1000 -0.0281
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.4231 nan 0.1000 10.4776
## 2 43.6174 nan 0.1000 8.8853
## 3 36.5281 nan 0.1000 6.8172
## 4 30.3330 nan 0.1000 6.1187
## 5 25.7279 nan 0.1000 4.5879
## 6 21.7777 nan 0.1000 3.9557
## 7 18.6932 nan 0.1000 2.8688
## 8 16.2063 nan 0.1000 2.3279
## 9 14.1763 nan 0.1000 2.1501
## 10 12.4376 nan 0.1000 1.5747
## 20 5.0274 nan 0.1000 0.1613
## 40 2.9364 nan 0.1000 -0.0381
## 60 2.3085 nan 0.1000 -0.0215
## 80 1.9718 nan 0.1000 -0.0501
## 100 1.6574 nan 0.1000 -0.0200
## 120 1.4683 nan 0.1000 -0.0246
## 140 1.2600 nan 0.1000 -0.0235
## 160 1.1154 nan 0.1000 -0.0274
## 180 0.9772 nan 0.1000 -0.0108
## 200 0.8796 nan 0.1000 -0.0109
## 220 0.7815 nan 0.1000 -0.0122
## 240 0.6943 nan 0.1000 -0.0106
## 260 0.6313 nan 0.1000 -0.0111
## 280 0.5660 nan 0.1000 -0.0107
## 300 0.5065 nan 0.1000 -0.0150
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.4970 nan 0.1000 10.8697
## 2 44.0576 nan 0.1000 9.3068
## 3 36.8649 nan 0.1000 6.4076
## 4 31.0434 nan 0.1000 5.0661
## 5 26.1874 nan 0.1000 4.9966
## 6 22.3797 nan 0.1000 3.8592
## 7 19.1280 nan 0.1000 2.8584
## 8 16.6271 nan 0.1000 2.2757
## 9 14.4346 nan 0.1000 2.4008
## 10 12.5175 nan 0.1000 1.8723
## 20 5.1001 nan 0.1000 0.1752
## 40 3.2941 nan 0.1000 -0.0273
## 60 2.7400 nan 0.1000 -0.0285
## 80 2.4257 nan 0.1000 -0.0495
## 100 2.1519 nan 0.1000 -0.0354
## 120 1.9314 nan 0.1000 -0.0213
## 140 1.7634 nan 0.1000 -0.0355
## 160 1.6108 nan 0.1000 -0.0276
## 180 1.4476 nan 0.1000 -0.0052
## 200 1.3215 nan 0.1000 -0.0201
## 220 1.2010 nan 0.1000 -0.0115
## 240 1.1009 nan 0.1000 -0.0314
## 260 1.0174 nan 0.1000 -0.0207
## 280 0.9310 nan 0.1000 -0.0099
## 300 0.8544 nan 0.1000 -0.0157
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.8931 nan 0.1000 10.7929
## 2 44.0418 nan 0.1000 8.6762
## 3 37.1766 nan 0.1000 6.8882
## 4 31.2344 nan 0.1000 6.0162
## 5 26.8118 nan 0.1000 4.1608
## 6 22.8499 nan 0.1000 3.4959
## 7 19.5087 nan 0.1000 3.1667
## 8 16.7145 nan 0.1000 2.4238
## 9 14.4300 nan 0.1000 1.8385
## 10 12.6616 nan 0.1000 1.7262
## 20 5.6587 nan 0.1000 0.1290
## 40 3.6989 nan 0.1000 -0.0200
## 60 3.2331 nan 0.1000 -0.0273
## 80 2.8993 nan 0.1000 -0.0444
## 100 2.6459 nan 0.1000 -0.0500
## 120 2.4334 nan 0.1000 -0.0359
## 140 2.2422 nan 0.1000 -0.0146
## 160 2.1009 nan 0.1000 -0.0271
## 180 1.9614 nan 0.1000 -0.0454
## 200 1.8339 nan 0.1000 -0.0387
## 220 1.7056 nan 0.1000 -0.0175
## 240 1.6171 nan 0.1000 -0.0167
## 260 1.5325 nan 0.1000 -0.0261
## 280 1.4494 nan 0.1000 -0.0204
## 300 1.3485 nan 0.1000 -0.0152
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.0136 nan 0.0100 0.7309
## 2 59.3069 nan 0.0100 0.7382
## 3 58.5576 nan 0.0100 0.7789
## 4 57.8037 nan 0.0100 0.7113
## 5 57.0877 nan 0.0100 0.7058
## 6 56.4809 nan 0.0100 0.6428
## 7 55.7992 nan 0.0100 0.6837
## 8 55.1057 nan 0.0100 0.6259
## 9 54.4941 nan 0.0100 0.6791
## 10 53.8237 nan 0.0100 0.6243
## 20 47.9664 nan 0.0100 0.5467
## 40 38.8344 nan 0.0100 0.3942
## 60 31.6753 nan 0.0100 0.2936
## 80 26.3466 nan 0.0100 0.2201
## 100 22.3660 nan 0.0100 0.1732
## 120 19.1525 nan 0.0100 0.1382
## 140 16.6830 nan 0.0100 0.0901
## 160 14.6963 nan 0.0100 0.0965
## 180 13.0591 nan 0.0100 0.0644
## 200 11.6676 nan 0.0100 0.0577
## 220 10.5019 nan 0.0100 0.0409
## 240 9.5397 nan 0.0100 0.0406
## 260 8.7367 nan 0.0100 0.0311
## 280 8.0795 nan 0.0100 0.0274
## 300 7.5040 nan 0.0100 0.0239
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.9436 nan 0.0100 0.7622
## 2 59.2310 nan 0.0100 0.7166
## 3 58.5180 nan 0.0100 0.7284
## 4 57.7595 nan 0.0100 0.7294
## 5 57.0582 nan 0.0100 0.7101
## 6 56.3601 nan 0.0100 0.6717
## 7 55.6781 nan 0.0100 0.6456
## 8 55.0569 nan 0.0100 0.6686
## 9 54.3782 nan 0.0100 0.6809
## 10 53.6954 nan 0.0100 0.6122
## 20 47.9674 nan 0.0100 0.4422
## 40 38.5254 nan 0.0100 0.4304
## 60 31.7045 nan 0.0100 0.3010
## 80 26.4296 nan 0.0100 0.2286
## 100 22.2645 nan 0.0100 0.1682
## 120 19.1053 nan 0.0100 0.1409
## 140 16.5966 nan 0.0100 0.1006
## 160 14.5532 nan 0.0100 0.0828
## 180 12.9567 nan 0.0100 0.0750
## 200 11.5875 nan 0.0100 0.0470
## 220 10.4623 nan 0.0100 0.0404
## 240 9.5353 nan 0.0100 0.0332
## 260 8.7254 nan 0.0100 0.0305
## 280 8.0461 nan 0.0100 0.0207
## 300 7.4677 nan 0.0100 0.0259
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.0122 nan 0.0100 0.7683
## 2 59.2449 nan 0.0100 0.6982
## 3 58.5086 nan 0.0100 0.6877
## 4 57.8015 nan 0.0100 0.7082
## 5 57.0913 nan 0.0100 0.6731
## 6 56.3931 nan 0.0100 0.6994
## 7 55.6996 nan 0.0100 0.6350
## 8 54.9988 nan 0.0100 0.6720
## 9 54.3109 nan 0.0100 0.6354
## 10 53.7220 nan 0.0100 0.6084
## 20 47.7270 nan 0.0100 0.5410
## 40 38.4607 nan 0.0100 0.3610
## 60 31.3869 nan 0.0100 0.2680
## 80 26.0707 nan 0.0100 0.2191
## 100 21.9812 nan 0.0100 0.1612
## 120 18.8080 nan 0.0100 0.1231
## 140 16.3560 nan 0.0100 0.0885
## 160 14.4122 nan 0.0100 0.0688
## 180 12.8133 nan 0.0100 0.0640
## 200 11.5218 nan 0.0100 0.0530
## 220 10.4511 nan 0.0100 0.0348
## 240 9.5459 nan 0.0100 0.0383
## 260 8.7682 nan 0.0100 0.0243
## 280 8.0921 nan 0.0100 0.0221
## 300 7.5235 nan 0.0100 0.0240
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.7489 nan 0.0100 1.0797
## 2 58.7624 nan 0.0100 0.9633
## 3 57.8081 nan 0.0100 0.9259
## 4 56.8097 nan 0.0100 0.9561
## 5 55.8406 nan 0.0100 0.9998
## 6 54.8998 nan 0.0100 0.9254
## 7 54.0152 nan 0.0100 0.8577
## 8 53.1138 nan 0.0100 0.8383
## 9 52.2625 nan 0.0100 0.8395
## 10 51.4682 nan 0.0100 0.8769
## 20 43.8430 nan 0.0100 0.6853
## 40 32.4891 nan 0.0100 0.4373
## 60 24.3596 nan 0.0100 0.3014
## 80 18.7091 nan 0.0100 0.2070
## 100 14.7317 nan 0.0100 0.1750
## 120 11.8547 nan 0.0100 0.1179
## 140 9.7365 nan 0.0100 0.0670
## 160 8.1844 nan 0.0100 0.0504
## 180 7.0230 nan 0.0100 0.0427
## 200 6.1941 nan 0.0100 0.0238
## 220 5.5471 nan 0.0100 0.0225
## 240 5.0435 nan 0.0100 0.0177
## 260 4.6756 nan 0.0100 0.0100
## 280 4.3899 nan 0.0100 0.0063
## 300 4.1639 nan 0.0100 0.0040
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.7124 nan 0.0100 0.8964
## 2 58.7423 nan 0.0100 0.9351
## 3 57.7704 nan 0.0100 0.9712
## 4 56.8275 nan 0.0100 0.9298
## 5 55.9085 nan 0.0100 0.9019
## 6 54.9836 nan 0.0100 0.9378
## 7 54.1080 nan 0.0100 0.9011
## 8 53.2389 nan 0.0100 0.8544
## 9 52.3379 nan 0.0100 0.9102
## 10 51.4581 nan 0.0100 0.8512
## 20 43.8869 nan 0.0100 0.6327
## 40 32.3847 nan 0.0100 0.4783
## 60 24.4073 nan 0.0100 0.2961
## 80 18.7973 nan 0.0100 0.2407
## 100 14.8049 nan 0.0100 0.1603
## 120 11.9158 nan 0.0100 0.1197
## 140 9.8633 nan 0.0100 0.0725
## 160 8.3504 nan 0.0100 0.0476
## 180 7.2150 nan 0.0100 0.0409
## 200 6.3375 nan 0.0100 0.0353
## 220 5.7002 nan 0.0100 0.0194
## 240 5.2224 nan 0.0100 0.0113
## 260 4.8668 nan 0.0100 0.0112
## 280 4.5955 nan 0.0100 0.0077
## 300 4.3983 nan 0.0100 0.0019
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.7504 nan 0.0100 0.9070
## 2 58.7920 nan 0.0100 0.8851
## 3 57.8570 nan 0.0100 0.9714
## 4 56.9265 nan 0.0100 0.9617
## 5 56.0434 nan 0.0100 0.9323
## 6 55.1533 nan 0.0100 0.9494
## 7 54.3481 nan 0.0100 0.9272
## 8 53.4239 nan 0.0100 0.8740
## 9 52.5853 nan 0.0100 0.8910
## 10 51.7667 nan 0.0100 0.8426
## 20 44.1633 nan 0.0100 0.7738
## 40 32.4893 nan 0.0100 0.4694
## 60 24.5213 nan 0.0100 0.3394
## 80 18.9678 nan 0.0100 0.2050
## 100 14.9668 nan 0.0100 0.1445
## 120 12.1689 nan 0.0100 0.1153
## 140 10.0862 nan 0.0100 0.0811
## 160 8.5776 nan 0.0100 0.0544
## 180 7.4089 nan 0.0100 0.0373
## 200 6.5669 nan 0.0100 0.0363
## 220 5.9359 nan 0.0100 0.0219
## 240 5.4538 nan 0.0100 0.0095
## 260 5.0953 nan 0.0100 0.0062
## 280 4.8104 nan 0.0100 0.0027
## 300 4.6044 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.7258 nan 0.0100 1.0260
## 2 58.6768 nan 0.0100 1.0375
## 3 57.6881 nan 0.0100 0.9698
## 4 56.6849 nan 0.0100 1.0019
## 5 55.6817 nan 0.0100 1.0072
## 6 54.7410 nan 0.0100 1.0024
## 7 53.7879 nan 0.0100 0.9439
## 8 52.8675 nan 0.0100 0.9711
## 9 51.9600 nan 0.0100 0.8284
## 10 51.0695 nan 0.0100 0.8600
## 20 43.0478 nan 0.0100 0.6777
## 40 30.9084 nan 0.0100 0.4363
## 60 22.6216 nan 0.0100 0.2841
## 80 16.9050 nan 0.0100 0.2333
## 100 12.9873 nan 0.0100 0.1665
## 120 10.2681 nan 0.0100 0.1101
## 140 8.2704 nan 0.0100 0.0788
## 160 6.8515 nan 0.0100 0.0613
## 180 5.8686 nan 0.0100 0.0225
## 200 5.1360 nan 0.0100 0.0168
## 220 4.6025 nan 0.0100 0.0188
## 240 4.2339 nan 0.0100 0.0094
## 260 3.9213 nan 0.0100 0.0007
## 280 3.6690 nan 0.0100 0.0025
## 300 3.4709 nan 0.0100 -0.0011
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.6912 nan 0.0100 1.1606
## 2 58.6333 nan 0.0100 0.9028
## 3 57.6481 nan 0.0100 0.9227
## 4 56.6834 nan 0.0100 0.9183
## 5 55.7213 nan 0.0100 0.9961
## 6 54.7459 nan 0.0100 0.9148
## 7 53.8130 nan 0.0100 0.8938
## 8 52.8851 nan 0.0100 0.8005
## 9 51.9532 nan 0.0100 0.8151
## 10 51.0693 nan 0.0100 0.8338
## 20 42.9955 nan 0.0100 0.6778
## 40 31.0907 nan 0.0100 0.4672
## 60 22.7488 nan 0.0100 0.3630
## 80 17.0070 nan 0.0100 0.2167
## 100 13.1385 nan 0.0100 0.1596
## 120 10.3236 nan 0.0100 0.1102
## 140 8.4081 nan 0.0100 0.0779
## 160 7.0312 nan 0.0100 0.0479
## 180 6.0379 nan 0.0100 0.0314
## 200 5.3245 nan 0.0100 0.0155
## 220 4.7935 nan 0.0100 0.0173
## 240 4.4352 nan 0.0100 0.0070
## 260 4.1571 nan 0.0100 0.0076
## 280 3.9369 nan 0.0100 0.0057
## 300 3.7738 nan 0.0100 0.0021
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.7164 nan 0.0100 1.0220
## 2 58.7366 nan 0.0100 0.9292
## 3 57.7222 nan 0.0100 0.9462
## 4 56.6980 nan 0.0100 0.9495
## 5 55.7850 nan 0.0100 0.9422
## 6 54.8432 nan 0.0100 0.8777
## 7 53.9257 nan 0.0100 1.0183
## 8 52.9730 nan 0.0100 0.9172
## 9 52.0858 nan 0.0100 0.8241
## 10 51.1973 nan 0.0100 0.9727
## 20 43.2499 nan 0.0100 0.6815
## 40 31.2662 nan 0.0100 0.4994
## 60 23.0804 nan 0.0100 0.3214
## 80 17.3888 nan 0.0100 0.2367
## 100 13.4122 nan 0.0100 0.1580
## 120 10.6160 nan 0.0100 0.1278
## 140 8.7383 nan 0.0100 0.0649
## 160 7.3541 nan 0.0100 0.0549
## 180 6.3414 nan 0.0100 0.0258
## 200 5.6619 nan 0.0100 0.0221
## 220 5.1349 nan 0.0100 0.0153
## 240 4.7506 nan 0.0100 0.0090
## 260 4.4798 nan 0.0100 0.0064
## 280 4.2722 nan 0.0100 0.0031
## 300 4.1111 nan 0.0100 0.0005
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.0708 nan 0.0500 3.4261
## 2 53.4832 nan 0.0500 3.5470
## 3 50.2492 nan 0.0500 2.9251
## 4 47.4588 nan 0.0500 2.7320
## 5 44.6253 nan 0.0500 2.3892
## 6 42.0264 nan 0.0500 2.2837
## 7 39.6164 nan 0.0500 2.2924
## 8 37.5523 nan 0.0500 1.8032
## 9 35.5230 nan 0.0500 1.7495
## 10 33.7832 nan 0.0500 1.7217
## 20 21.4582 nan 0.0500 0.7238
## 40 11.1680 nan 0.0500 0.3059
## 60 7.2943 nan 0.0500 0.0969
## 80 5.5371 nan 0.0500 0.0298
## 100 4.7182 nan 0.0500 0.0174
## 120 4.2944 nan 0.0500 -0.0006
## 140 4.0854 nan 0.0500 0.0033
## 160 3.9731 nan 0.0500 0.0011
## 180 3.9094 nan 0.0500 -0.0373
## 200 3.8431 nan 0.0500 0.0023
## 220 3.7720 nan 0.0500 -0.0119
## 240 3.7190 nan 0.0500 -0.0093
## 260 3.6729 nan 0.0500 -0.0129
## 280 3.6199 nan 0.0500 -0.0257
## 300 3.5688 nan 0.0500 -0.0191
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.9279 nan 0.0500 3.5968
## 2 53.2888 nan 0.0500 3.3480
## 3 50.0576 nan 0.0500 2.6050
## 4 47.4279 nan 0.0500 2.7749
## 5 44.9536 nan 0.0500 2.2471
## 6 42.6819 nan 0.0500 2.3080
## 7 40.6400 nan 0.0500 2.2747
## 8 38.3433 nan 0.0500 2.1405
## 9 36.4497 nan 0.0500 1.8345
## 10 34.6595 nan 0.0500 1.7560
## 20 21.9080 nan 0.0500 0.8333
## 40 11.3024 nan 0.0500 0.2667
## 60 7.3082 nan 0.0500 0.0769
## 80 5.5560 nan 0.0500 0.0589
## 100 4.7648 nan 0.0500 0.0268
## 120 4.3887 nan 0.0500 -0.0072
## 140 4.2133 nan 0.0500 -0.0085
## 160 4.0883 nan 0.0500 -0.0011
## 180 3.9976 nan 0.0500 -0.0048
## 200 3.9324 nan 0.0500 -0.0069
## 220 3.8944 nan 0.0500 -0.0134
## 240 3.8400 nan 0.0500 -0.0030
## 260 3.7719 nan 0.0500 -0.0172
## 280 3.7233 nan 0.0500 -0.0166
## 300 3.6749 nan 0.0500 -0.0062
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.8978 nan 0.0500 3.7894
## 2 53.5460 nan 0.0500 3.3779
## 3 50.9201 nan 0.0500 2.5757
## 4 48.0603 nan 0.0500 2.7026
## 5 45.2111 nan 0.0500 2.7884
## 6 42.6579 nan 0.0500 2.0706
## 7 40.2014 nan 0.0500 2.2035
## 8 38.0148 nan 0.0500 1.9890
## 9 36.1214 nan 0.0500 1.8842
## 10 34.1316 nan 0.0500 1.7787
## 20 21.6834 nan 0.0500 0.9314
## 40 11.1737 nan 0.0500 0.2354
## 60 7.2628 nan 0.0500 0.0347
## 80 5.5891 nan 0.0500 0.0537
## 100 4.8670 nan 0.0500 0.0175
## 120 4.5663 nan 0.0500 -0.0026
## 140 4.3998 nan 0.0500 0.0058
## 160 4.2818 nan 0.0500 -0.0066
## 180 4.2154 nan 0.0500 -0.0180
## 200 4.1390 nan 0.0500 -0.0084
## 220 4.0767 nan 0.0500 -0.0062
## 240 4.0111 nan 0.0500 -0.0099
## 260 3.9545 nan 0.0500 -0.0106
## 280 3.9060 nan 0.0500 -0.0047
## 300 3.8599 nan 0.0500 -0.0144
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.9425 nan 0.0500 4.4165
## 2 51.6150 nan 0.0500 4.2477
## 3 47.5709 nan 0.0500 3.8185
## 4 44.0024 nan 0.0500 3.5616
## 5 40.4063 nan 0.0500 3.2855
## 6 37.4479 nan 0.0500 2.9129
## 7 34.7746 nan 0.0500 2.9527
## 8 32.3206 nan 0.0500 2.2662
## 9 29.9910 nan 0.0500 2.2748
## 10 27.7969 nan 0.0500 2.3176
## 20 14.6070 nan 0.0500 0.7513
## 40 6.2589 nan 0.0500 0.0918
## 60 4.1484 nan 0.0500 0.0122
## 80 3.5259 nan 0.0500 -0.0068
## 100 3.1920 nan 0.0500 -0.0192
## 120 2.9503 nan 0.0500 -0.0159
## 140 2.7679 nan 0.0500 -0.0069
## 160 2.5788 nan 0.0500 -0.0014
## 180 2.4560 nan 0.0500 -0.0029
## 200 2.3124 nan 0.0500 -0.0062
## 220 2.1909 nan 0.0500 -0.0118
## 240 2.1008 nan 0.0500 -0.0078
## 260 2.0215 nan 0.0500 -0.0095
## 280 1.9458 nan 0.0500 -0.0138
## 300 1.8626 nan 0.0500 -0.0176
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.9850 nan 0.0500 4.9956
## 2 51.5332 nan 0.0500 4.4692
## 3 47.4913 nan 0.0500 4.3128
## 4 43.8275 nan 0.0500 3.6534
## 5 40.5928 nan 0.0500 3.5577
## 6 37.6119 nan 0.0500 3.1624
## 7 34.7157 nan 0.0500 2.4115
## 8 32.0531 nan 0.0500 2.4976
## 9 29.7861 nan 0.0500 2.2356
## 10 27.6384 nan 0.0500 1.8172
## 20 14.7108 nan 0.0500 0.6873
## 40 6.5437 nan 0.0500 0.1095
## 60 4.5010 nan 0.0500 0.0318
## 80 3.9269 nan 0.0500 -0.0094
## 100 3.5592 nan 0.0500 -0.0372
## 120 3.3482 nan 0.0500 -0.0346
## 140 3.1675 nan 0.0500 -0.0130
## 160 3.0351 nan 0.0500 -0.0165
## 180 2.9170 nan 0.0500 -0.0175
## 200 2.7937 nan 0.0500 -0.0070
## 220 2.6939 nan 0.0500 -0.0158
## 240 2.5863 nan 0.0500 -0.0158
## 260 2.4855 nan 0.0500 -0.0146
## 280 2.3889 nan 0.0500 -0.0096
## 300 2.3010 nan 0.0500 -0.0093
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.7401 nan 0.0500 4.9635
## 2 51.3754 nan 0.0500 4.7747
## 3 47.3313 nan 0.0500 4.1007
## 4 43.5089 nan 0.0500 3.7418
## 5 39.8664 nan 0.0500 3.4419
## 6 36.7988 nan 0.0500 2.9628
## 7 33.9349 nan 0.0500 2.6719
## 8 31.3412 nan 0.0500 2.4528
## 9 29.0799 nan 0.0500 2.1439
## 10 27.0874 nan 0.0500 2.1597
## 20 14.5006 nan 0.0500 0.8585
## 40 6.5885 nan 0.0500 0.1377
## 60 4.6493 nan 0.0500 -0.0094
## 80 4.1056 nan 0.0500 -0.0054
## 100 3.8363 nan 0.0500 -0.0279
## 120 3.6283 nan 0.0500 -0.0136
## 140 3.4383 nan 0.0500 -0.0174
## 160 3.3067 nan 0.0500 -0.0139
## 180 3.1766 nan 0.0500 -0.0075
## 200 3.0857 nan 0.0500 -0.0393
## 220 2.9728 nan 0.0500 -0.0330
## 240 2.8943 nan 0.0500 -0.0329
## 260 2.8106 nan 0.0500 -0.0060
## 280 2.7267 nan 0.0500 -0.0197
## 300 2.6547 nan 0.0500 -0.0091
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.5695 nan 0.0500 4.8853
## 2 51.0947 nan 0.0500 4.5775
## 3 46.7267 nan 0.0500 4.6384
## 4 42.8285 nan 0.0500 3.5816
## 5 39.3509 nan 0.0500 3.3451
## 6 36.1016 nan 0.0500 2.9419
## 7 33.0778 nan 0.0500 2.7151
## 8 30.4066 nan 0.0500 2.6578
## 9 28.1277 nan 0.0500 1.8531
## 10 26.0370 nan 0.0500 2.1431
## 20 12.7724 nan 0.0500 0.6495
## 40 5.1641 nan 0.0500 0.0681
## 60 3.4869 nan 0.0500 0.0085
## 80 2.8689 nan 0.0500 -0.0127
## 100 2.5049 nan 0.0500 -0.0189
## 120 2.2382 nan 0.0500 -0.0128
## 140 2.0354 nan 0.0500 -0.0146
## 160 1.8743 nan 0.0500 -0.0186
## 180 1.7375 nan 0.0500 -0.0200
## 200 1.5886 nan 0.0500 -0.0122
## 220 1.4566 nan 0.0500 -0.0089
## 240 1.3520 nan 0.0500 -0.0075
## 260 1.2580 nan 0.0500 -0.0072
## 280 1.1705 nan 0.0500 -0.0143
## 300 1.0886 nan 0.0500 -0.0108
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.7056 nan 0.0500 5.2085
## 2 50.8091 nan 0.0500 4.4483
## 3 46.6120 nan 0.0500 4.1828
## 4 42.7302 nan 0.0500 4.0813
## 5 39.3565 nan 0.0500 2.9791
## 6 36.1900 nan 0.0500 2.8864
## 7 33.2310 nan 0.0500 2.5093
## 8 30.7194 nan 0.0500 2.3333
## 9 28.3601 nan 0.0500 2.2850
## 10 26.2464 nan 0.0500 2.1626
## 20 13.1996 nan 0.0500 0.7463
## 40 5.4008 nan 0.0500 0.0814
## 60 3.8696 nan 0.0500 -0.0022
## 80 3.3145 nan 0.0500 -0.0160
## 100 3.0309 nan 0.0500 -0.0381
## 120 2.8319 nan 0.0500 -0.0083
## 140 2.6408 nan 0.0500 -0.0087
## 160 2.4663 nan 0.0500 -0.0138
## 180 2.3286 nan 0.0500 -0.0124
## 200 2.1742 nan 0.0500 -0.0277
## 220 2.0430 nan 0.0500 -0.0089
## 240 1.9449 nan 0.0500 -0.0173
## 260 1.8509 nan 0.0500 -0.0158
## 280 1.7439 nan 0.0500 -0.0144
## 300 1.6656 nan 0.0500 -0.0160
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.7311 nan 0.0500 4.9251
## 2 51.1347 nan 0.0500 4.9513
## 3 46.8736 nan 0.0500 4.0580
## 4 43.2034 nan 0.0500 3.8862
## 5 39.5175 nan 0.0500 3.2606
## 6 36.4749 nan 0.0500 3.3231
## 7 33.6722 nan 0.0500 2.7951
## 8 31.1216 nan 0.0500 2.5245
## 9 28.9134 nan 0.0500 2.2811
## 10 26.7782 nan 0.0500 2.0307
## 20 13.2069 nan 0.0500 0.7264
## 40 5.5949 nan 0.0500 0.1197
## 60 4.1884 nan 0.0500 -0.0012
## 80 3.7078 nan 0.0500 -0.0077
## 100 3.4398 nan 0.0500 -0.0144
## 120 3.2301 nan 0.0500 -0.0226
## 140 3.0666 nan 0.0500 -0.0096
## 160 2.8969 nan 0.0500 -0.0150
## 180 2.7509 nan 0.0500 -0.0088
## 200 2.6578 nan 0.0500 -0.0292
## 220 2.5397 nan 0.0500 -0.0141
## 240 2.4359 nan 0.0500 -0.0224
## 260 2.3378 nan 0.0500 -0.0136
## 280 2.2502 nan 0.0500 -0.0277
## 300 2.1721 nan 0.0500 -0.0121
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.0201 nan 0.1000 6.7912
## 2 47.1671 nan 0.1000 5.6943
## 3 41.8649 nan 0.1000 4.6413
## 4 37.3717 nan 0.1000 3.9270
## 5 33.4679 nan 0.1000 3.7025
## 6 30.7213 nan 0.1000 2.6611
## 7 27.9469 nan 0.1000 2.6493
## 8 25.6977 nan 0.1000 2.1255
## 9 23.1097 nan 0.1000 1.9221
## 10 21.0413 nan 0.1000 1.6492
## 20 11.1598 nan 0.1000 0.4762
## 40 5.4967 nan 0.1000 0.0876
## 60 4.3596 nan 0.1000 -0.0277
## 80 4.0585 nan 0.1000 -0.0126
## 100 3.9124 nan 0.1000 -0.0106
## 120 3.8047 nan 0.1000 -0.0418
## 140 3.6896 nan 0.1000 -0.0394
## 160 3.5624 nan 0.1000 -0.0304
## 180 3.4733 nan 0.1000 -0.0154
## 200 3.4134 nan 0.1000 -0.0360
## 220 3.3151 nan 0.1000 0.0020
## 240 3.2590 nan 0.1000 -0.0441
## 260 3.1976 nan 0.1000 -0.0249
## 280 3.1588 nan 0.1000 -0.0069
## 300 3.0971 nan 0.1000 -0.0161
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.9720 nan 0.1000 7.0820
## 2 47.8281 nan 0.1000 5.5254
## 3 43.0021 nan 0.1000 4.5666
## 4 38.2168 nan 0.1000 4.3922
## 5 33.9648 nan 0.1000 3.4568
## 6 30.6128 nan 0.1000 3.2502
## 7 27.7217 nan 0.1000 2.7963
## 8 25.6289 nan 0.1000 1.8686
## 9 23.4901 nan 0.1000 1.9565
## 10 21.6803 nan 0.1000 1.6614
## 20 11.0081 nan 0.1000 0.4699
## 40 5.6507 nan 0.1000 0.1134
## 60 4.5026 nan 0.1000 0.0090
## 80 4.2631 nan 0.1000 -0.0388
## 100 4.0980 nan 0.1000 -0.0167
## 120 3.9801 nan 0.1000 -0.0244
## 140 3.8476 nan 0.1000 -0.0189
## 160 3.7643 nan 0.1000 -0.0034
## 180 3.6628 nan 0.1000 -0.0406
## 200 3.5835 nan 0.1000 -0.0117
## 220 3.5044 nan 0.1000 -0.0109
## 240 3.4446 nan 0.1000 -0.0361
## 260 3.3838 nan 0.1000 -0.0078
## 280 3.3332 nan 0.1000 -0.0151
## 300 3.2810 nan 0.1000 -0.0139
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.2314 nan 0.1000 5.7575
## 2 47.3634 nan 0.1000 5.7790
## 3 41.9419 nan 0.1000 5.3284
## 4 37.8054 nan 0.1000 4.3571
## 5 33.7300 nan 0.1000 3.0227
## 6 30.5260 nan 0.1000 3.4160
## 7 27.9784 nan 0.1000 2.5033
## 8 25.6028 nan 0.1000 2.3520
## 9 23.8093 nan 0.1000 1.6925
## 10 21.9359 nan 0.1000 1.8866
## 20 11.3268 nan 0.1000 0.6500
## 40 5.6763 nan 0.1000 0.1065
## 60 4.6272 nan 0.1000 0.0288
## 80 4.3596 nan 0.1000 -0.0253
## 100 4.1986 nan 0.1000 -0.0032
## 120 4.0725 nan 0.1000 -0.0292
## 140 3.9706 nan 0.1000 -0.0367
## 160 3.9017 nan 0.1000 -0.0261
## 180 3.8187 nan 0.1000 -0.0203
## 200 3.7687 nan 0.1000 -0.0258
## 220 3.6867 nan 0.1000 0.0019
## 240 3.6335 nan 0.1000 -0.0391
## 260 3.5575 nan 0.1000 -0.0036
## 280 3.4870 nan 0.1000 -0.0101
## 300 3.4292 nan 0.1000 -0.0392
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.6126 nan 0.1000 9.7468
## 2 43.6817 nan 0.1000 8.1560
## 3 36.8655 nan 0.1000 6.3558
## 4 31.2082 nan 0.1000 4.5009
## 5 26.5961 nan 0.1000 3.7140
## 6 22.8896 nan 0.1000 3.6687
## 7 19.6821 nan 0.1000 2.7012
## 8 17.3127 nan 0.1000 2.0202
## 9 15.3611 nan 0.1000 1.6812
## 10 13.7626 nan 0.1000 1.4007
## 20 6.0800 nan 0.1000 0.2733
## 40 3.6523 nan 0.1000 -0.0383
## 60 3.0679 nan 0.1000 -0.0400
## 80 2.6484 nan 0.1000 -0.0421
## 100 2.3701 nan 0.1000 -0.0294
## 120 2.1359 nan 0.1000 -0.0119
## 140 1.9004 nan 0.1000 -0.0120
## 160 1.7491 nan 0.1000 -0.0176
## 180 1.5818 nan 0.1000 -0.0052
## 200 1.4524 nan 0.1000 -0.0187
## 220 1.3678 nan 0.1000 -0.0142
## 240 1.2733 nan 0.1000 -0.0144
## 260 1.2029 nan 0.1000 -0.0167
## 280 1.1294 nan 0.1000 -0.0155
## 300 1.0542 nan 0.1000 -0.0157
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.2698 nan 0.1000 9.5994
## 2 43.8665 nan 0.1000 7.5947
## 3 37.3628 nan 0.1000 6.3916
## 4 31.9483 nan 0.1000 4.5749
## 5 27.5093 nan 0.1000 4.6154
## 6 24.0422 nan 0.1000 3.1865
## 7 20.9710 nan 0.1000 2.8786
## 8 18.5160 nan 0.1000 1.9100
## 9 16.5711 nan 0.1000 1.7528
## 10 14.8592 nan 0.1000 1.7289
## 20 6.3916 nan 0.1000 0.3117
## 40 3.9074 nan 0.1000 -0.0486
## 60 3.4775 nan 0.1000 -0.0747
## 80 3.1672 nan 0.1000 -0.0339
## 100 2.9344 nan 0.1000 -0.0468
## 120 2.7406 nan 0.1000 -0.0289
## 140 2.5548 nan 0.1000 -0.0593
## 160 2.3731 nan 0.1000 -0.0294
## 180 2.1705 nan 0.1000 -0.0301
## 200 2.0504 nan 0.1000 -0.0356
## 220 1.9382 nan 0.1000 -0.0232
## 240 1.8042 nan 0.1000 -0.0123
## 260 1.6814 nan 0.1000 -0.0280
## 280 1.5893 nan 0.1000 -0.0283
## 300 1.5040 nan 0.1000 -0.0090
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.1814 nan 0.1000 9.9841
## 2 43.5471 nan 0.1000 7.9245
## 3 37.1071 nan 0.1000 6.3505
## 4 31.8947 nan 0.1000 5.1912
## 5 27.5006 nan 0.1000 4.5199
## 6 23.8010 nan 0.1000 3.1701
## 7 20.7672 nan 0.1000 2.7958
## 8 18.4720 nan 0.1000 2.0899
## 9 16.3873 nan 0.1000 2.0008
## 10 14.4815 nan 0.1000 1.6456
## 20 6.5498 nan 0.1000 0.3072
## 40 4.1843 nan 0.1000 -0.0065
## 60 3.6697 nan 0.1000 -0.0241
## 80 3.4116 nan 0.1000 0.0037
## 100 3.2172 nan 0.1000 -0.0608
## 120 3.0223 nan 0.1000 -0.0571
## 140 2.8498 nan 0.1000 -0.0301
## 160 2.6892 nan 0.1000 -0.0272
## 180 2.5411 nan 0.1000 -0.0468
## 200 2.3915 nan 0.1000 -0.0205
## 220 2.3031 nan 0.1000 -0.0556
## 240 2.1949 nan 0.1000 -0.0098
## 260 2.0899 nan 0.1000 -0.0178
## 280 1.9812 nan 0.1000 -0.0209
## 300 1.9129 nan 0.1000 -0.0177
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.2967 nan 0.1000 10.4184
## 2 43.0139 nan 0.1000 7.6793
## 3 35.8131 nan 0.1000 6.2183
## 4 30.1520 nan 0.1000 4.5974
## 5 25.4157 nan 0.1000 5.1502
## 6 21.8744 nan 0.1000 3.7899
## 7 18.7071 nan 0.1000 3.1930
## 8 16.1025 nan 0.1000 2.6241
## 9 13.9893 nan 0.1000 1.9805
## 10 12.5347 nan 0.1000 1.5300
## 20 4.9977 nan 0.1000 0.2533
## 40 2.9033 nan 0.1000 -0.0265
## 60 2.2183 nan 0.1000 -0.0443
## 80 1.8877 nan 0.1000 -0.0629
## 100 1.5792 nan 0.1000 -0.0167
## 120 1.3367 nan 0.1000 -0.0291
## 140 1.1469 nan 0.1000 -0.0117
## 160 0.9912 nan 0.1000 -0.0103
## 180 0.8554 nan 0.1000 -0.0120
## 200 0.7589 nan 0.1000 -0.0074
## 220 0.6743 nan 0.1000 -0.0129
## 240 0.6083 nan 0.1000 -0.0085
## 260 0.5374 nan 0.1000 -0.0076
## 280 0.4791 nan 0.1000 -0.0085
## 300 0.4312 nan 0.1000 -0.0073
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.5501 nan 0.1000 10.3366
## 2 42.0344 nan 0.1000 8.3214
## 3 35.0822 nan 0.1000 7.2918
## 4 29.7430 nan 0.1000 5.0071
## 5 25.0099 nan 0.1000 3.8199
## 6 21.2212 nan 0.1000 3.4886
## 7 18.4426 nan 0.1000 2.4005
## 8 16.0871 nan 0.1000 2.1322
## 9 13.9715 nan 0.1000 2.1783
## 10 12.3125 nan 0.1000 1.3296
## 20 5.0131 nan 0.1000 0.1336
## 40 3.2769 nan 0.1000 -0.0559
## 60 2.7738 nan 0.1000 -0.0476
## 80 2.4876 nan 0.1000 -0.0527
## 100 2.2089 nan 0.1000 -0.0375
## 120 1.9899 nan 0.1000 -0.0262
## 140 1.8270 nan 0.1000 -0.0396
## 160 1.6009 nan 0.1000 -0.0525
## 180 1.4766 nan 0.1000 -0.0362
## 200 1.3451 nan 0.1000 -0.0142
## 220 1.2159 nan 0.1000 -0.0174
## 240 1.1201 nan 0.1000 -0.0062
## 260 1.0292 nan 0.1000 -0.0183
## 280 0.9507 nan 0.1000 -0.0123
## 300 0.8717 nan 0.1000 -0.0074
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.4377 nan 0.1000 10.0612
## 2 42.2709 nan 0.1000 8.2933
## 3 35.7439 nan 0.1000 6.9922
## 4 29.9256 nan 0.1000 5.6780
## 5 25.5106 nan 0.1000 3.9175
## 6 21.9648 nan 0.1000 3.3536
## 7 19.2097 nan 0.1000 2.7483
## 8 16.5308 nan 0.1000 2.3399
## 9 14.4251 nan 0.1000 1.9576
## 10 12.7110 nan 0.1000 1.8204
## 20 5.5993 nan 0.1000 0.2558
## 40 3.8591 nan 0.1000 -0.0401
## 60 3.3687 nan 0.1000 -0.0628
## 80 3.0184 nan 0.1000 -0.0439
## 100 2.7602 nan 0.1000 -0.0416
## 120 2.5498 nan 0.1000 -0.0279
## 140 2.3568 nan 0.1000 -0.0183
## 160 2.1967 nan 0.1000 -0.0228
## 180 2.0427 nan 0.1000 -0.0190
## 200 1.9039 nan 0.1000 -0.0362
## 220 1.7758 nan 0.1000 -0.0369
## 240 1.6617 nan 0.1000 -0.0217
## 260 1.5450 nan 0.1000 -0.0273
## 280 1.4692 nan 0.1000 -0.0114
## 300 1.3721 nan 0.1000 -0.0124
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.7802 nan 0.0100 0.7357
## 2 59.0511 nan 0.0100 0.7323
## 3 58.3037 nan 0.0100 0.6932
## 4 57.5961 nan 0.0100 0.6842
## 5 56.9363 nan 0.0100 0.6610
## 6 56.2517 nan 0.0100 0.6939
## 7 55.6626 nan 0.0100 0.6442
## 8 55.0153 nan 0.0100 0.6660
## 9 54.3390 nan 0.0100 0.6426
## 10 53.6719 nan 0.0100 0.6308
## 20 47.7516 nan 0.0100 0.5090
## 40 38.5617 nan 0.0100 0.3554
## 60 31.9074 nan 0.0100 0.2400
## 80 26.8206 nan 0.0100 0.2481
## 100 22.7279 nan 0.0100 0.1786
## 120 19.5876 nan 0.0100 0.1309
## 140 17.1873 nan 0.0100 0.1063
## 160 15.2162 nan 0.0100 0.0885
## 180 13.6377 nan 0.0100 0.0699
## 200 12.3013 nan 0.0100 0.0474
## 220 11.1336 nan 0.0100 0.0368
## 240 10.2211 nan 0.0100 0.0341
## 260 9.4366 nan 0.0100 0.0278
## 280 8.7483 nan 0.0100 0.0226
## 300 8.1385 nan 0.0100 0.0196
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.8481 nan 0.0100 0.7078
## 2 59.1059 nan 0.0100 0.7472
## 3 58.4287 nan 0.0100 0.7632
## 4 57.7299 nan 0.0100 0.6834
## 5 57.1300 nan 0.0100 0.6425
## 6 56.4520 nan 0.0100 0.6361
## 7 55.7836 nan 0.0100 0.6218
## 8 55.1044 nan 0.0100 0.5973
## 9 54.4765 nan 0.0100 0.6439
## 10 53.8710 nan 0.0100 0.6292
## 20 47.9486 nan 0.0100 0.5116
## 40 38.7935 nan 0.0100 0.3702
## 60 31.9034 nan 0.0100 0.2722
## 80 26.7048 nan 0.0100 0.2088
## 100 22.6756 nan 0.0100 0.1636
## 120 19.5336 nan 0.0100 0.0773
## 140 17.1238 nan 0.0100 0.1133
## 160 15.1701 nan 0.0100 0.0734
## 180 13.5280 nan 0.0100 0.0471
## 200 12.1957 nan 0.0100 0.0429
## 220 11.0711 nan 0.0100 0.0465
## 240 10.1066 nan 0.0100 0.0370
## 260 9.3002 nan 0.0100 0.0371
## 280 8.6126 nan 0.0100 0.0263
## 300 8.0320 nan 0.0100 0.0165
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.8285 nan 0.0100 0.7512
## 2 59.0799 nan 0.0100 0.6581
## 3 58.3778 nan 0.0100 0.7019
## 4 57.6990 nan 0.0100 0.6918
## 5 56.9748 nan 0.0100 0.7173
## 6 56.2622 nan 0.0100 0.6385
## 7 55.5478 nan 0.0100 0.6622
## 8 54.8353 nan 0.0100 0.6592
## 9 54.1843 nan 0.0100 0.6116
## 10 53.5202 nan 0.0100 0.5804
## 20 47.7961 nan 0.0100 0.4823
## 40 38.7859 nan 0.0100 0.4111
## 60 32.0661 nan 0.0100 0.2158
## 80 26.7199 nan 0.0100 0.2148
## 100 22.6224 nan 0.0100 0.1511
## 120 19.5549 nan 0.0100 0.1094
## 140 17.0971 nan 0.0100 0.0986
## 160 15.1495 nan 0.0100 0.0808
## 180 13.5290 nan 0.0100 0.0554
## 200 12.2197 nan 0.0100 0.0438
## 220 11.1515 nan 0.0100 0.0385
## 240 10.2507 nan 0.0100 0.0347
## 260 9.4569 nan 0.0100 0.0268
## 280 8.8141 nan 0.0100 0.0222
## 300 8.2600 nan 0.0100 0.0260
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.5648 nan 0.0100 0.9044
## 2 58.6228 nan 0.0100 0.8239
## 3 57.7422 nan 0.0100 0.8790
## 4 56.8614 nan 0.0100 0.9225
## 5 55.9960 nan 0.0100 0.8283
## 6 55.1309 nan 0.0100 0.8546
## 7 54.1854 nan 0.0100 0.8393
## 8 53.3071 nan 0.0100 0.8552
## 9 52.4641 nan 0.0100 0.8578
## 10 51.6414 nan 0.0100 0.8407
## 20 44.1650 nan 0.0100 0.6280
## 40 32.7486 nan 0.0100 0.4340
## 60 24.6903 nan 0.0100 0.3268
## 80 19.0837 nan 0.0100 0.2384
## 100 15.1189 nan 0.0100 0.1512
## 120 12.2264 nan 0.0100 0.1064
## 140 10.1455 nan 0.0100 0.0819
## 160 8.5828 nan 0.0100 0.0560
## 180 7.3886 nan 0.0100 0.0495
## 200 6.5366 nan 0.0100 0.0363
## 220 5.8293 nan 0.0100 0.0200
## 240 5.3174 nan 0.0100 0.0156
## 260 4.9191 nan 0.0100 0.0101
## 280 4.6012 nan 0.0100 0.0056
## 300 4.3540 nan 0.0100 0.0035
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.5817 nan 0.0100 0.9281
## 2 58.5747 nan 0.0100 0.9947
## 3 57.6325 nan 0.0100 0.9581
## 4 56.7099 nan 0.0100 0.8773
## 5 55.8143 nan 0.0100 0.9412
## 6 54.9267 nan 0.0100 0.8364
## 7 54.0580 nan 0.0100 0.8937
## 8 53.2065 nan 0.0100 0.8718
## 9 52.3735 nan 0.0100 0.8637
## 10 51.5661 nan 0.0100 0.7839
## 20 44.0263 nan 0.0100 0.6910
## 40 32.5168 nan 0.0100 0.4663
## 60 24.6245 nan 0.0100 0.3795
## 80 19.0941 nan 0.0100 0.1941
## 100 15.1076 nan 0.0100 0.1754
## 120 12.2536 nan 0.0100 0.1118
## 140 10.1873 nan 0.0100 0.0859
## 160 8.6745 nan 0.0100 0.0573
## 180 7.4842 nan 0.0100 0.0458
## 200 6.6422 nan 0.0100 0.0350
## 220 5.9885 nan 0.0100 0.0114
## 240 5.4857 nan 0.0100 0.0135
## 260 5.0890 nan 0.0100 0.0083
## 280 4.7837 nan 0.0100 0.0040
## 300 4.5383 nan 0.0100 0.0013
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.5857 nan 0.0100 0.9209
## 2 58.5667 nan 0.0100 0.8821
## 3 57.6433 nan 0.0100 0.9076
## 4 56.7130 nan 0.0100 0.8524
## 5 55.7961 nan 0.0100 0.9505
## 6 54.9478 nan 0.0100 0.7982
## 7 54.0999 nan 0.0100 0.9649
## 8 53.2687 nan 0.0100 0.9195
## 9 52.4163 nan 0.0100 0.7728
## 10 51.6273 nan 0.0100 0.8040
## 20 44.0618 nan 0.0100 0.6643
## 40 32.7286 nan 0.0100 0.4396
## 60 25.0251 nan 0.0100 0.3287
## 80 19.4036 nan 0.0100 0.2325
## 100 15.4938 nan 0.0100 0.1422
## 120 12.6591 nan 0.0100 0.0958
## 140 10.6122 nan 0.0100 0.0734
## 160 9.0626 nan 0.0100 0.0541
## 180 7.9023 nan 0.0100 0.0488
## 200 7.0198 nan 0.0100 0.0331
## 220 6.3613 nan 0.0100 0.0293
## 240 5.8480 nan 0.0100 0.0178
## 260 5.4388 nan 0.0100 0.0058
## 280 5.1535 nan 0.0100 0.0098
## 300 4.9236 nan 0.0100 0.0025
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.5101 nan 0.0100 0.9904
## 2 58.4918 nan 0.0100 1.0195
## 3 57.5132 nan 0.0100 0.9932
## 4 56.4853 nan 0.0100 1.0311
## 5 55.5356 nan 0.0100 0.9417
## 6 54.5637 nan 0.0100 0.9455
## 7 53.6098 nan 0.0100 0.9397
## 8 52.7132 nan 0.0100 0.7587
## 9 51.7853 nan 0.0100 0.9676
## 10 50.9311 nan 0.0100 0.7840
## 20 43.1773 nan 0.0100 0.6776
## 40 31.3077 nan 0.0100 0.4155
## 60 23.0982 nan 0.0100 0.2787
## 80 17.2931 nan 0.0100 0.2317
## 100 13.3724 nan 0.0100 0.1454
## 120 10.5697 nan 0.0100 0.1123
## 140 8.5455 nan 0.0100 0.0773
## 160 7.0736 nan 0.0100 0.0459
## 180 6.0550 nan 0.0100 0.0277
## 200 5.2960 nan 0.0100 0.0195
## 220 4.7491 nan 0.0100 0.0189
## 240 4.3263 nan 0.0100 0.0039
## 260 4.0006 nan 0.0100 0.0060
## 280 3.7255 nan 0.0100 0.0021
## 300 3.5140 nan 0.0100 0.0003
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.4967 nan 0.0100 1.0480
## 2 58.5022 nan 0.0100 1.0844
## 3 57.4810 nan 0.0100 0.9313
## 4 56.5314 nan 0.0100 0.9639
## 5 55.4949 nan 0.0100 0.9813
## 6 54.5464 nan 0.0100 0.9588
## 7 53.7010 nan 0.0100 0.9853
## 8 52.7920 nan 0.0100 0.8156
## 9 51.8629 nan 0.0100 0.7145
## 10 50.9695 nan 0.0100 0.8524
## 20 43.2519 nan 0.0100 0.7130
## 40 31.1532 nan 0.0100 0.4942
## 60 23.0077 nan 0.0100 0.3057
## 80 17.3690 nan 0.0100 0.2364
## 100 13.4476 nan 0.0100 0.1563
## 120 10.7219 nan 0.0100 0.0936
## 140 8.7297 nan 0.0100 0.0689
## 160 7.3428 nan 0.0100 0.0412
## 180 6.3344 nan 0.0100 0.0357
## 200 5.5801 nan 0.0100 0.0224
## 220 5.0212 nan 0.0100 0.0163
## 240 4.6111 nan 0.0100 0.0061
## 260 4.3105 nan 0.0100 -0.0012
## 280 4.0639 nan 0.0100 0.0045
## 300 3.8846 nan 0.0100 -0.0016
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.5488 nan 0.0100 0.9925
## 2 58.5097 nan 0.0100 0.9841
## 3 57.5452 nan 0.0100 0.8939
## 4 56.5743 nan 0.0100 0.9740
## 5 55.6588 nan 0.0100 0.9509
## 6 54.7102 nan 0.0100 0.8030
## 7 53.7678 nan 0.0100 0.9736
## 8 52.8105 nan 0.0100 0.7952
## 9 51.9219 nan 0.0100 0.8898
## 10 51.0551 nan 0.0100 0.8353
## 20 43.1001 nan 0.0100 0.7881
## 40 31.1791 nan 0.0100 0.4949
## 60 23.1325 nan 0.0100 0.2781
## 80 17.6630 nan 0.0100 0.2352
## 100 13.7272 nan 0.0100 0.1388
## 120 11.0270 nan 0.0100 0.0967
## 140 9.1150 nan 0.0100 0.0683
## 160 7.7501 nan 0.0100 0.0476
## 180 6.7380 nan 0.0100 0.0327
## 200 6.0165 nan 0.0100 0.0292
## 220 5.4903 nan 0.0100 0.0121
## 240 5.1109 nan 0.0100 0.0114
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## 300 4.4026 nan 0.0100 0.0021
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.7872 nan 0.0500 3.6766
## 2 53.2926 nan 0.0500 3.3236
## 3 50.0704 nan 0.0500 3.0223
## 4 47.6810 nan 0.0500 2.5563
## 5 45.3960 nan 0.0500 2.4589
## 6 42.9093 nan 0.0500 2.6793
## 7 40.5233 nan 0.0500 2.0671
## 8 38.5083 nan 0.0500 1.9015
## 9 36.6530 nan 0.0500 1.7910
## 10 34.8013 nan 0.0500 1.6317
## 20 22.6614 nan 0.0500 0.7718
## 40 12.0838 nan 0.0500 0.2686
## 60 7.9134 nan 0.0500 0.0918
## 80 6.0996 nan 0.0500 0.0308
## 100 5.1189 nan 0.0500 0.0385
## 120 4.6183 nan 0.0500 -0.0121
## 140 4.3756 nan 0.0500 -0.0003
## 160 4.2279 nan 0.0500 -0.0058
## 180 4.1508 nan 0.0500 -0.0153
## 200 4.0516 nan 0.0500 -0.0040
## 220 3.9592 nan 0.0500 -0.0304
## 240 3.9001 nan 0.0500 -0.0211
## 260 3.8370 nan 0.0500 -0.0336
## 280 3.7849 nan 0.0500 -0.0051
## 300 3.7344 nan 0.0500 -0.0206
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.6918 nan 0.0500 3.6445
## 2 53.5021 nan 0.0500 3.2461
## 3 50.2657 nan 0.0500 3.1762
## 4 47.6085 nan 0.0500 2.5090
## 5 44.8983 nan 0.0500 2.5774
## 6 42.6877 nan 0.0500 2.3453
## 7 40.5744 nan 0.0500 2.0587
## 8 38.4281 nan 0.0500 2.1082
## 9 36.3937 nan 0.0500 1.6577
## 10 34.5292 nan 0.0500 1.6177
## 20 22.5464 nan 0.0500 0.8994
## 40 12.1597 nan 0.0500 0.2889
## 60 8.1875 nan 0.0500 0.1153
## 80 6.2349 nan 0.0500 0.0581
## 100 5.3274 nan 0.0500 0.0296
## 120 4.8910 nan 0.0500 0.0066
## 140 4.6459 nan 0.0500 0.0034
## 160 4.4969 nan 0.0500 0.0005
## 180 4.3977 nan 0.0500 -0.0102
## 200 4.3036 nan 0.0500 0.0016
## 220 4.2196 nan 0.0500 -0.0091
## 240 4.1582 nan 0.0500 -0.0180
## 260 4.1127 nan 0.0500 -0.0065
## 280 4.0679 nan 0.0500 -0.0132
## 300 4.0204 nan 0.0500 -0.0115
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.8755 nan 0.0500 3.5794
## 2 53.7424 nan 0.0500 3.2878
## 3 50.6522 nan 0.0500 2.9833
## 4 47.9544 nan 0.0500 2.6395
## 5 45.4138 nan 0.0500 2.7439
## 6 42.8427 nan 0.0500 2.3994
## 7 40.7187 nan 0.0500 1.9552
## 8 38.7492 nan 0.0500 2.1480
## 9 36.9457 nan 0.0500 1.8365
## 10 35.1804 nan 0.0500 1.6570
## 20 22.7692 nan 0.0500 0.8627
## 40 12.1790 nan 0.0500 0.2706
## 60 8.2834 nan 0.0500 0.1099
## 80 6.4187 nan 0.0500 0.0150
## 100 5.5844 nan 0.0500 0.0312
## 120 5.1343 nan 0.0500 -0.0045
## 140 4.9207 nan 0.0500 0.0031
## 160 4.7661 nan 0.0500 -0.0061
## 180 4.6628 nan 0.0500 -0.0155
## 200 4.5723 nan 0.0500 0.0018
## 220 4.4969 nan 0.0500 -0.0043
## 240 4.4312 nan 0.0500 -0.0180
## 260 4.3907 nan 0.0500 -0.0033
## 280 4.3181 nan 0.0500 -0.0060
## 300 4.2693 nan 0.0500 -0.0094
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.5723 nan 0.0500 5.4751
## 2 51.2485 nan 0.0500 5.1950
## 3 47.3208 nan 0.0500 4.0114
## 4 43.6778 nan 0.0500 3.6748
## 5 40.5033 nan 0.0500 3.1818
## 6 37.4376 nan 0.0500 2.7304
## 7 34.6650 nan 0.0500 2.6490
## 8 32.0696 nan 0.0500 2.6748
## 9 29.8378 nan 0.0500 2.3310
## 10 27.8292 nan 0.0500 1.9549
## 20 14.7525 nan 0.0500 0.7964
## 40 6.3952 nan 0.0500 0.1315
## 60 4.3550 nan 0.0500 0.0326
## 80 3.6489 nan 0.0500 -0.0245
## 100 3.2780 nan 0.0500 -0.0102
## 120 3.0935 nan 0.0500 -0.0151
## 140 2.9114 nan 0.0500 -0.0059
## 160 2.7534 nan 0.0500 -0.0015
## 180 2.6077 nan 0.0500 -0.0138
## 200 2.4788 nan 0.0500 -0.0100
## 220 2.3569 nan 0.0500 -0.0035
## 240 2.2370 nan 0.0500 -0.0250
## 260 2.1518 nan 0.0500 -0.0152
## 280 2.0678 nan 0.0500 -0.0143
## 300 1.9833 nan 0.0500 -0.0192
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.4885 nan 0.0500 4.4900
## 2 51.3147 nan 0.0500 4.1321
## 3 47.6127 nan 0.0500 3.8366
## 4 44.0584 nan 0.0500 3.3136
## 5 40.7248 nan 0.0500 3.1548
## 6 37.8650 nan 0.0500 2.8126
## 7 34.9879 nan 0.0500 2.7318
## 8 32.5627 nan 0.0500 2.4684
## 9 30.2428 nan 0.0500 2.3586
## 10 28.2083 nan 0.0500 2.0613
## 20 14.9852 nan 0.0500 0.7585
## 40 6.5936 nan 0.0500 0.1921
## 60 4.5482 nan 0.0500 0.0183
## 80 3.9252 nan 0.0500 0.0021
## 100 3.6024 nan 0.0500 -0.0133
## 120 3.3923 nan 0.0500 -0.0066
## 140 3.2153 nan 0.0500 -0.0156
## 160 3.0899 nan 0.0500 -0.0239
## 180 2.9587 nan 0.0500 -0.0074
## 200 2.8516 nan 0.0500 -0.0246
## 220 2.7438 nan 0.0500 -0.0141
## 240 2.6329 nan 0.0500 -0.0048
## 260 2.5240 nan 0.0500 -0.0081
## 280 2.4396 nan 0.0500 -0.0062
## 300 2.3470 nan 0.0500 -0.0112
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.4685 nan 0.0500 4.4892
## 2 51.1385 nan 0.0500 3.8802
## 3 47.2735 nan 0.0500 3.9465
## 4 43.7178 nan 0.0500 3.0013
## 5 40.2901 nan 0.0500 3.1289
## 6 37.4141 nan 0.0500 2.8831
## 7 34.7332 nan 0.0500 2.7797
## 8 32.2570 nan 0.0500 2.3107
## 9 30.1355 nan 0.0500 2.1410
## 10 28.0738 nan 0.0500 1.8346
## 20 15.4129 nan 0.0500 0.6502
## 40 6.9285 nan 0.0500 0.1711
## 60 4.9471 nan 0.0500 0.0396
## 80 4.3511 nan 0.0500 -0.0267
## 100 4.0525 nan 0.0500 -0.0272
## 120 3.8253 nan 0.0500 -0.0603
## 140 3.6731 nan 0.0500 -0.0169
## 160 3.4932 nan 0.0500 -0.0080
## 180 3.3284 nan 0.0500 -0.0295
## 200 3.2131 nan 0.0500 -0.0124
## 220 3.1113 nan 0.0500 -0.0350
## 240 2.9976 nan 0.0500 -0.0142
## 260 2.8845 nan 0.0500 -0.0079
## 280 2.8048 nan 0.0500 -0.0185
## 300 2.7210 nan 0.0500 -0.0304
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.4433 nan 0.0500 4.6871
## 2 50.6566 nan 0.0500 4.4558
## 3 46.3076 nan 0.0500 4.8334
## 4 42.5614 nan 0.0500 3.4451
## 5 39.3690 nan 0.0500 3.2278
## 6 36.2337 nan 0.0500 2.7395
## 7 33.4289 nan 0.0500 2.6358
## 8 30.8133 nan 0.0500 2.5092
## 9 28.5011 nan 0.0500 2.1957
## 10 26.3029 nan 0.0500 2.0283
## 20 13.2484 nan 0.0500 0.7241
## 40 5.3378 nan 0.0500 0.0719
## 60 3.6090 nan 0.0500 0.0016
## 80 2.9822 nan 0.0500 0.0006
## 100 2.6148 nan 0.0500 -0.0128
## 120 2.3507 nan 0.0500 -0.0235
## 140 2.1540 nan 0.0500 -0.0133
## 160 1.9864 nan 0.0500 -0.0144
## 180 1.8405 nan 0.0500 -0.0140
## 200 1.6762 nan 0.0500 -0.0036
## 220 1.5499 nan 0.0500 -0.0067
## 240 1.4413 nan 0.0500 -0.0130
## 260 1.3428 nan 0.0500 -0.0096
## 280 1.2527 nan 0.0500 -0.0069
## 300 1.1683 nan 0.0500 -0.0093
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.5656 nan 0.0500 4.9245
## 2 50.9659 nan 0.0500 4.5981
## 3 46.7109 nan 0.0500 4.0706
## 4 43.0318 nan 0.0500 3.6478
## 5 39.5982 nan 0.0500 3.5874
## 6 36.2520 nan 0.0500 2.8755
## 7 33.6210 nan 0.0500 2.8678
## 8 31.0300 nan 0.0500 2.3172
## 9 28.6586 nan 0.0500 2.4129
## 10 26.4788 nan 0.0500 2.0020
## 20 13.3763 nan 0.0500 0.8368
## 40 5.4650 nan 0.0500 0.1065
## 60 3.8814 nan 0.0500 -0.0001
## 80 3.3449 nan 0.0500 -0.0239
## 100 2.9858 nan 0.0500 -0.0180
## 120 2.7205 nan 0.0500 -0.0177
## 140 2.5219 nan 0.0500 -0.0300
## 160 2.3445 nan 0.0500 -0.0133
## 180 2.2079 nan 0.0500 -0.0272
## 200 2.0839 nan 0.0500 -0.0065
## 220 1.9516 nan 0.0500 -0.0068
## 240 1.8472 nan 0.0500 -0.0146
## 260 1.7748 nan 0.0500 -0.0011
## 280 1.6891 nan 0.0500 -0.0134
## 300 1.6065 nan 0.0500 -0.0099
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.5143 nan 0.0500 4.9780
## 2 51.0793 nan 0.0500 4.4238
## 3 46.8782 nan 0.0500 3.8999
## 4 42.8993 nan 0.0500 3.9868
## 5 39.6739 nan 0.0500 3.1584
## 6 36.5759 nan 0.0500 2.9466
## 7 33.8833 nan 0.0500 2.6507
## 8 31.1700 nan 0.0500 2.2807
## 9 28.9239 nan 0.0500 2.4934
## 10 26.7299 nan 0.0500 2.2882
## 20 13.5371 nan 0.0500 0.8964
## 40 5.8025 nan 0.0500 0.0847
## 60 4.3024 nan 0.0500 0.0119
## 80 3.8625 nan 0.0500 -0.0205
## 100 3.5652 nan 0.0500 -0.0098
## 120 3.3974 nan 0.0500 -0.0242
## 140 3.1970 nan 0.0500 -0.0252
## 160 3.0094 nan 0.0500 -0.0187
## 180 2.8756 nan 0.0500 -0.0313
## 200 2.7444 nan 0.0500 -0.0184
## 220 2.6012 nan 0.0500 -0.0145
## 240 2.5021 nan 0.0500 -0.0072
## 260 2.4029 nan 0.0500 -0.0191
## 280 2.3015 nan 0.0500 -0.0163
## 300 2.1953 nan 0.0500 -0.0154
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.9176 nan 0.1000 7.0151
## 2 47.9532 nan 0.1000 5.5853
## 3 42.8926 nan 0.1000 4.8187
## 4 38.7388 nan 0.1000 3.8364
## 5 35.3977 nan 0.1000 3.6935
## 6 31.8647 nan 0.1000 3.4015
## 7 28.8960 nan 0.1000 2.6856
## 8 26.3654 nan 0.1000 2.1380
## 9 24.0134 nan 0.1000 1.9854
## 10 21.9709 nan 0.1000 2.0209
## 20 11.6744 nan 0.1000 0.4276
## 40 6.0735 nan 0.1000 0.1003
## 60 4.7100 nan 0.1000 0.0161
## 80 4.2892 nan 0.1000 -0.0251
## 100 4.1021 nan 0.1000 -0.0057
## 120 3.9508 nan 0.1000 -0.0309
## 140 3.8789 nan 0.1000 -0.0226
## 160 3.7843 nan 0.1000 -0.0029
## 180 3.6989 nan 0.1000 -0.0568
## 200 3.6470 nan 0.1000 -0.0236
## 220 3.5851 nan 0.1000 -0.0365
## 240 3.5099 nan 0.1000 -0.0345
## 260 3.4418 nan 0.1000 -0.0083
## 280 3.3784 nan 0.1000 -0.0396
## 300 3.3314 nan 0.1000 -0.0175
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.4171 nan 0.1000 6.6449
## 2 47.8300 nan 0.1000 5.6766
## 3 42.7724 nan 0.1000 3.8795
## 4 37.8536 nan 0.1000 4.4030
## 5 34.0978 nan 0.1000 3.5786
## 6 31.2104 nan 0.1000 2.0190
## 7 28.4670 nan 0.1000 2.4757
## 8 26.0355 nan 0.1000 1.9452
## 9 23.5444 nan 0.1000 2.2629
## 10 21.6720 nan 0.1000 1.8464
## 20 11.5154 nan 0.1000 0.5737
## 40 6.0920 nan 0.1000 0.0317
## 60 4.8708 nan 0.1000 -0.0191
## 80 4.5732 nan 0.1000 -0.0479
## 100 4.3684 nan 0.1000 -0.0290
## 120 4.2346 nan 0.1000 -0.0405
## 140 4.0871 nan 0.1000 -0.0198
## 160 3.9922 nan 0.1000 -0.0035
## 180 3.9139 nan 0.1000 -0.0402
## 200 3.8492 nan 0.1000 0.0051
## 220 3.7670 nan 0.1000 -0.0235
## 240 3.6923 nan 0.1000 -0.0083
## 260 3.6539 nan 0.1000 -0.0194
## 280 3.6077 nan 0.1000 -0.0291
## 300 3.5643 nan 0.1000 -0.0177
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.3800 nan 0.1000 7.0036
## 2 47.3159 nan 0.1000 6.1114
## 3 42.3875 nan 0.1000 5.2513
## 4 38.2181 nan 0.1000 3.8459
## 5 34.5876 nan 0.1000 3.6150
## 6 31.1349 nan 0.1000 3.0653
## 7 28.4608 nan 0.1000 2.6255
## 8 26.1302 nan 0.1000 2.3775
## 9 24.1076 nan 0.1000 1.9873
## 10 22.0113 nan 0.1000 2.1248
## 20 11.5955 nan 0.1000 0.4241
## 40 6.1258 nan 0.1000 0.0968
## 60 5.0537 nan 0.1000 -0.0656
## 80 4.6700 nan 0.1000 -0.0060
## 100 4.4820 nan 0.1000 -0.0005
## 120 4.3614 nan 0.1000 -0.0008
## 140 4.2283 nan 0.1000 -0.0178
## 160 4.1440 nan 0.1000 -0.0267
## 180 4.0592 nan 0.1000 -0.0315
## 200 3.9786 nan 0.1000 -0.0142
## 220 3.9305 nan 0.1000 -0.0163
## 240 3.8593 nan 0.1000 -0.0138
## 260 3.8084 nan 0.1000 -0.0116
## 280 3.7356 nan 0.1000 0.0007
## 300 3.6903 nan 0.1000 -0.0268
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.1298 nan 0.1000 9.2221
## 2 43.5610 nan 0.1000 8.6046
## 3 37.1065 nan 0.1000 6.0624
## 4 32.0674 nan 0.1000 5.0817
## 5 27.6947 nan 0.1000 4.5593
## 6 23.9011 nan 0.1000 3.4295
## 7 20.6683 nan 0.1000 3.1512
## 8 18.1921 nan 0.1000 1.8080
## 9 16.1280 nan 0.1000 2.1147
## 10 14.3391 nan 0.1000 1.4602
## 20 6.3692 nan 0.1000 0.2999
## 40 3.7310 nan 0.1000 0.0111
## 60 3.1778 nan 0.1000 -0.0836
## 80 2.7711 nan 0.1000 -0.0146
## 100 2.4656 nan 0.1000 -0.0200
## 120 2.2339 nan 0.1000 -0.0297
## 140 2.0230 nan 0.1000 -0.0246
## 160 1.8985 nan 0.1000 -0.0155
## 180 1.7470 nan 0.1000 -0.0117
## 200 1.6409 nan 0.1000 -0.0190
## 220 1.5303 nan 0.1000 -0.0177
## 240 1.4277 nan 0.1000 -0.0230
## 260 1.3392 nan 0.1000 -0.0174
## 280 1.2283 nan 0.1000 -0.0142
## 300 1.1600 nan 0.1000 -0.0167
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.9346 nan 0.1000 9.2742
## 2 43.2298 nan 0.1000 7.0547
## 3 36.4575 nan 0.1000 5.9730
## 4 31.0990 nan 0.1000 5.0957
## 5 27.0145 nan 0.1000 3.9160
## 6 23.5909 nan 0.1000 3.4839
## 7 20.4472 nan 0.1000 2.6777
## 8 17.8799 nan 0.1000 2.4049
## 9 15.7463 nan 0.1000 1.9826
## 10 14.2068 nan 0.1000 1.5676
## 20 6.2886 nan 0.1000 0.3123
## 40 3.9425 nan 0.1000 -0.0292
## 60 3.4702 nan 0.1000 -0.0300
## 80 3.1008 nan 0.1000 -0.0131
## 100 2.8624 nan 0.1000 -0.0200
## 120 2.6313 nan 0.1000 -0.0598
## 140 2.4602 nan 0.1000 -0.0303
## 160 2.3188 nan 0.1000 -0.0175
## 180 2.1772 nan 0.1000 -0.0405
## 200 2.0384 nan 0.1000 -0.0226
## 220 1.9101 nan 0.1000 -0.0192
## 240 1.7834 nan 0.1000 -0.0352
## 260 1.6996 nan 0.1000 -0.0160
## 280 1.6113 nan 0.1000 -0.0279
## 300 1.5246 nan 0.1000 -0.0093
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.5950 nan 0.1000 8.3794
## 2 43.8308 nan 0.1000 7.1214
## 3 37.8076 nan 0.1000 5.6016
## 4 32.8698 nan 0.1000 5.3205
## 5 27.8841 nan 0.1000 4.6785
## 6 24.5715 nan 0.1000 3.3179
## 7 21.5397 nan 0.1000 2.8568
## 8 18.8432 nan 0.1000 2.4496
## 9 16.8221 nan 0.1000 2.0181
## 10 15.2617 nan 0.1000 1.7046
## 20 6.6347 nan 0.1000 0.2833
## 40 4.2486 nan 0.1000 -0.0149
## 60 3.7787 nan 0.1000 -0.0190
## 80 3.4694 nan 0.1000 -0.0196
## 100 3.1947 nan 0.1000 -0.0373
## 120 3.0221 nan 0.1000 -0.0334
## 140 2.8385 nan 0.1000 -0.0276
## 160 2.6808 nan 0.1000 -0.0184
## 180 2.5116 nan 0.1000 -0.0285
## 200 2.4002 nan 0.1000 -0.0232
## 220 2.2783 nan 0.1000 -0.0380
## 240 2.1822 nan 0.1000 -0.0200
## 260 2.0864 nan 0.1000 -0.0368
## 280 1.9896 nan 0.1000 -0.0156
## 300 1.8993 nan 0.1000 -0.0295
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.6054 nan 0.1000 10.1424
## 2 42.5885 nan 0.1000 6.7176
## 3 35.6261 nan 0.1000 6.5610
## 4 30.0676 nan 0.1000 4.9483
## 5 25.4522 nan 0.1000 4.2897
## 6 21.8942 nan 0.1000 3.6321
## 7 18.8629 nan 0.1000 2.6835
## 8 16.4111 nan 0.1000 2.4666
## 9 14.4325 nan 0.1000 2.1045
## 10 12.4454 nan 0.1000 1.8635
## 20 5.2680 nan 0.1000 0.1775
## 40 3.0517 nan 0.1000 -0.0332
## 60 2.3445 nan 0.1000 -0.0274
## 80 1.9347 nan 0.1000 -0.0353
## 100 1.6053 nan 0.1000 -0.0128
## 120 1.3895 nan 0.1000 -0.0223
## 140 1.2440 nan 0.1000 -0.0083
## 160 1.0853 nan 0.1000 -0.0087
## 180 0.9643 nan 0.1000 -0.0029
## 200 0.8466 nan 0.1000 -0.0149
## 220 0.7593 nan 0.1000 -0.0125
## 240 0.6760 nan 0.1000 -0.0065
## 260 0.6214 nan 0.1000 -0.0128
## 280 0.5510 nan 0.1000 -0.0057
## 300 0.4948 nan 0.1000 -0.0075
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 49.9634 nan 0.1000 9.4014
## 2 41.8847 nan 0.1000 6.7158
## 3 35.2613 nan 0.1000 6.8687
## 4 29.7116 nan 0.1000 5.1108
## 5 25.2942 nan 0.1000 4.0963
## 6 21.4687 nan 0.1000 3.5762
## 7 18.4948 nan 0.1000 2.6667
## 8 15.9559 nan 0.1000 2.1789
## 9 13.9846 nan 0.1000 1.7370
## 10 12.4357 nan 0.1000 1.6308
## 20 5.1818 nan 0.1000 0.1900
## 40 3.2671 nan 0.1000 -0.0104
## 60 2.7578 nan 0.1000 -0.0256
## 80 2.4273 nan 0.1000 -0.0440
## 100 2.1215 nan 0.1000 -0.0201
## 120 1.8934 nan 0.1000 -0.0193
## 140 1.6978 nan 0.1000 -0.0406
## 160 1.5561 nan 0.1000 -0.0241
## 180 1.4241 nan 0.1000 -0.0314
## 200 1.2936 nan 0.1000 -0.0115
## 220 1.1826 nan 0.1000 -0.0127
## 240 1.0977 nan 0.1000 -0.0059
## 260 1.0046 nan 0.1000 -0.0160
## 280 0.9342 nan 0.1000 -0.0112
## 300 0.8744 nan 0.1000 -0.0290
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.6462 nan 0.1000 9.4627
## 2 42.5121 nan 0.1000 7.6239
## 3 35.8743 nan 0.1000 6.7384
## 4 30.4848 nan 0.1000 4.9400
## 5 26.1078 nan 0.1000 4.3720
## 6 22.3408 nan 0.1000 3.3748
## 7 19.2267 nan 0.1000 2.7493
## 8 16.7132 nan 0.1000 2.5066
## 9 14.7997 nan 0.1000 1.7164
## 10 13.1754 nan 0.1000 1.4000
## 20 5.9738 nan 0.1000 0.2272
## 40 3.9478 nan 0.1000 -0.0150
## 60 3.4497 nan 0.1000 -0.0536
## 80 3.1351 nan 0.1000 -0.0345
## 100 2.8915 nan 0.1000 -0.0718
## 120 2.6538 nan 0.1000 -0.0334
## 140 2.4287 nan 0.1000 -0.0142
## 160 2.2201 nan 0.1000 -0.0211
## 180 2.0823 nan 0.1000 -0.0655
## 200 1.9182 nan 0.1000 -0.0218
## 220 1.7647 nan 0.1000 -0.0211
## 240 1.6355 nan 0.1000 -0.0192
## 260 1.5266 nan 0.1000 -0.0150
## 280 1.4334 nan 0.1000 -0.0190
## 300 1.3407 nan 0.1000 -0.0257
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.3666 nan 0.0100 0.7745
## 2 61.6090 nan 0.0100 0.7517
## 3 60.8320 nan 0.0100 0.7918
## 4 60.0690 nan 0.0100 0.7150
## 5 59.3565 nan 0.0100 0.7037
## 6 58.6184 nan 0.0100 0.6815
## 7 57.8756 nan 0.0100 0.7089
## 8 57.2192 nan 0.0100 0.6890
## 9 56.5969 nan 0.0100 0.6257
## 10 55.9429 nan 0.0100 0.6358
## 20 49.7310 nan 0.0100 0.5790
## 40 40.1863 nan 0.0100 0.4189
## 60 32.9604 nan 0.0100 0.2811
## 80 27.5049 nan 0.0100 0.2147
## 100 23.3075 nan 0.0100 0.1674
## 120 20.0439 nan 0.0100 0.1249
## 140 17.4202 nan 0.0100 0.0916
## 160 15.3069 nan 0.0100 0.0914
## 180 13.5655 nan 0.0100 0.0534
## 200 12.1620 nan 0.0100 0.0569
## 220 10.9455 nan 0.0100 0.0415
## 240 9.9352 nan 0.0100 0.0391
## 260 9.0962 nan 0.0100 0.0338
## 280 8.3855 nan 0.0100 0.0300
## 300 7.7680 nan 0.0100 0.0221
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.3263 nan 0.0100 0.7230
## 2 61.6125 nan 0.0100 0.7691
## 3 60.8592 nan 0.0100 0.7790
## 4 60.0890 nan 0.0100 0.7706
## 5 59.3343 nan 0.0100 0.6986
## 6 58.6245 nan 0.0100 0.7201
## 7 57.8838 nan 0.0100 0.7145
## 8 57.1900 nan 0.0100 0.7245
## 9 56.4230 nan 0.0100 0.7275
## 10 55.7747 nan 0.0100 0.6913
## 20 49.6832 nan 0.0100 0.5411
## 40 40.0053 nan 0.0100 0.3880
## 60 32.9294 nan 0.0100 0.2949
## 80 27.5121 nan 0.0100 0.2086
## 100 23.2315 nan 0.0100 0.1756
## 120 19.8692 nan 0.0100 0.1261
## 140 17.2753 nan 0.0100 0.1054
## 160 15.1819 nan 0.0100 0.0764
## 180 13.4942 nan 0.0100 0.0621
## 200 12.1124 nan 0.0100 0.0519
## 220 10.9416 nan 0.0100 0.0515
## 240 9.9380 nan 0.0100 0.0171
## 260 9.0848 nan 0.0100 0.0393
## 280 8.3595 nan 0.0100 0.0307
## 300 7.7409 nan 0.0100 0.0142
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.4301 nan 0.0100 0.7655
## 2 61.6800 nan 0.0100 0.7735
## 3 60.8625 nan 0.0100 0.7237
## 4 60.0639 nan 0.0100 0.7596
## 5 59.3196 nan 0.0100 0.7467
## 6 58.6305 nan 0.0100 0.6892
## 7 57.8902 nan 0.0100 0.7108
## 8 57.2107 nan 0.0100 0.6974
## 9 56.4986 nan 0.0100 0.6530
## 10 55.8773 nan 0.0100 0.6445
## 20 49.8659 nan 0.0100 0.5837
## 40 40.3298 nan 0.0100 0.4291
## 60 33.0077 nan 0.0100 0.2939
## 80 27.5587 nan 0.0100 0.2352
## 100 23.2846 nan 0.0100 0.1626
## 120 19.9623 nan 0.0100 0.1516
## 140 17.3520 nan 0.0100 0.0961
## 160 15.2743 nan 0.0100 0.0711
## 180 13.6101 nan 0.0100 0.0628
## 200 12.1664 nan 0.0100 0.0507
## 220 11.0031 nan 0.0100 0.0404
## 240 10.0059 nan 0.0100 0.0360
## 260 9.1981 nan 0.0100 0.0285
## 280 8.5058 nan 0.0100 0.0207
## 300 7.9298 nan 0.0100 0.0208
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.1448 nan 0.0100 1.0570
## 2 61.1426 nan 0.0100 0.9295
## 3 60.1010 nan 0.0100 0.9063
## 4 59.1199 nan 0.0100 0.8452
## 5 58.1789 nan 0.0100 1.0006
## 6 57.2737 nan 0.0100 0.9247
## 7 56.3663 nan 0.0100 0.9373
## 8 55.4725 nan 0.0100 0.8479
## 9 54.5851 nan 0.0100 0.8785
## 10 53.7240 nan 0.0100 0.8542
## 20 45.8532 nan 0.0100 0.7787
## 40 33.7258 nan 0.0100 0.5471
## 60 25.3079 nan 0.0100 0.3943
## 80 19.3238 nan 0.0100 0.2046
## 100 15.1052 nan 0.0100 0.1924
## 120 12.0922 nan 0.0100 0.1081
## 140 9.8888 nan 0.0100 0.0924
## 160 8.3051 nan 0.0100 0.0500
## 180 7.1167 nan 0.0100 0.0381
## 200 6.2350 nan 0.0100 0.0351
## 220 5.5166 nan 0.0100 0.0268
## 240 4.9871 nan 0.0100 0.0219
## 260 4.5872 nan 0.0100 0.0122
## 280 4.2805 nan 0.0100 0.0053
## 300 4.0400 nan 0.0100 0.0096
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.0945 nan 0.0100 0.9786
## 2 61.1058 nan 0.0100 0.9705
## 3 60.0819 nan 0.0100 0.9370
## 4 59.1443 nan 0.0100 0.9501
## 5 58.1405 nan 0.0100 1.0091
## 6 57.2354 nan 0.0100 0.8829
## 7 56.3284 nan 0.0100 0.9700
## 8 55.4571 nan 0.0100 0.8453
## 9 54.5712 nan 0.0100 0.9180
## 10 53.7421 nan 0.0100 0.8607
## 20 45.8756 nan 0.0100 0.7534
## 40 33.6686 nan 0.0100 0.5340
## 60 25.2229 nan 0.0100 0.3554
## 80 19.3627 nan 0.0100 0.2164
## 100 15.1921 nan 0.0100 0.1725
## 120 12.2147 nan 0.0100 0.1181
## 140 9.9922 nan 0.0100 0.0832
## 160 8.4040 nan 0.0100 0.0616
## 180 7.2069 nan 0.0100 0.0563
## 200 6.3044 nan 0.0100 0.0246
## 220 5.6315 nan 0.0100 0.0232
## 240 5.0794 nan 0.0100 0.0218
## 260 4.6806 nan 0.0100 0.0092
## 280 4.3936 nan 0.0100 0.0122
## 300 4.1563 nan 0.0100 0.0073
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.1150 nan 0.0100 0.9482
## 2 61.0986 nan 0.0100 1.0356
## 3 60.0872 nan 0.0100 0.9700
## 4 59.0997 nan 0.0100 0.9243
## 5 58.0800 nan 0.0100 0.8601
## 6 57.1403 nan 0.0100 0.9063
## 7 56.2267 nan 0.0100 0.9634
## 8 55.2867 nan 0.0100 0.8690
## 9 54.3811 nan 0.0100 0.8761
## 10 53.5286 nan 0.0100 0.8338
## 20 45.7749 nan 0.0100 0.7665
## 40 33.5810 nan 0.0100 0.5015
## 60 25.3404 nan 0.0100 0.3192
## 80 19.4822 nan 0.0100 0.2446
## 100 15.3706 nan 0.0100 0.1522
## 120 12.3885 nan 0.0100 0.1043
## 140 10.1963 nan 0.0100 0.0853
## 160 8.5583 nan 0.0100 0.0634
## 180 7.3577 nan 0.0100 0.0370
## 200 6.4635 nan 0.0100 0.0328
## 220 5.8216 nan 0.0100 0.0234
## 240 5.2938 nan 0.0100 0.0191
## 260 4.9117 nan 0.0100 0.0092
## 280 4.6263 nan 0.0100 0.0061
## 300 4.4120 nan 0.0100 0.0036
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.0231 nan 0.0100 1.1812
## 2 60.9618 nan 0.0100 1.1027
## 3 59.9310 nan 0.0100 0.9604
## 4 58.8539 nan 0.0100 1.0465
## 5 57.7915 nan 0.0100 0.9646
## 6 56.7643 nan 0.0100 1.0248
## 7 55.7950 nan 0.0100 0.9430
## 8 54.7986 nan 0.0100 0.8944
## 9 53.8359 nan 0.0100 0.8376
## 10 52.8706 nan 0.0100 1.0157
## 20 44.5943 nan 0.0100 0.7622
## 40 31.9935 nan 0.0100 0.5424
## 60 23.3223 nan 0.0100 0.3732
## 80 17.3687 nan 0.0100 0.2161
## 100 13.3079 nan 0.0100 0.1714
## 120 10.4287 nan 0.0100 0.1139
## 140 8.3224 nan 0.0100 0.0582
## 160 6.8508 nan 0.0100 0.0498
## 180 5.7816 nan 0.0100 0.0445
## 200 5.0393 nan 0.0100 0.0229
## 220 4.4853 nan 0.0100 0.0100
## 240 4.0663 nan 0.0100 0.0108
## 260 3.7599 nan 0.0100 -0.0015
## 280 3.5120 nan 0.0100 0.0030
## 300 3.3146 nan 0.0100 0.0028
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.0327 nan 0.0100 1.0138
## 2 60.9327 nan 0.0100 1.0485
## 3 59.8853 nan 0.0100 1.0677
## 4 58.8338 nan 0.0100 0.8701
## 5 57.8401 nan 0.0100 1.0097
## 6 56.8605 nan 0.0100 0.8884
## 7 55.8420 nan 0.0100 0.9328
## 8 54.8976 nan 0.0100 0.9608
## 9 53.9937 nan 0.0100 0.9336
## 10 53.1256 nan 0.0100 0.9155
## 20 44.7059 nan 0.0100 0.6464
## 40 32.1114 nan 0.0100 0.4689
## 60 23.5668 nan 0.0100 0.3216
## 80 17.6377 nan 0.0100 0.2522
## 100 13.4722 nan 0.0100 0.1564
## 120 10.5870 nan 0.0100 0.1114
## 140 8.5470 nan 0.0100 0.0702
## 160 7.0802 nan 0.0100 0.0473
## 180 6.0294 nan 0.0100 0.0389
## 200 5.2730 nan 0.0100 0.0299
## 220 4.7071 nan 0.0100 0.0189
## 240 4.3004 nan 0.0100 0.0146
## 260 3.9658 nan 0.0100 0.0064
## 280 3.7325 nan 0.0100 0.0039
## 300 3.5570 nan 0.0100 0.0004
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.0248 nan 0.0100 1.1522
## 2 60.9473 nan 0.0100 1.1067
## 3 59.8982 nan 0.0100 1.1449
## 4 58.9010 nan 0.0100 1.0017
## 5 57.9225 nan 0.0100 0.9997
## 6 56.9768 nan 0.0100 0.9790
## 7 56.0655 nan 0.0100 0.9277
## 8 55.1037 nan 0.0100 0.9313
## 9 54.1773 nan 0.0100 0.9572
## 10 53.2365 nan 0.0100 0.8576
## 20 45.0227 nan 0.0100 0.6748
## 40 32.5234 nan 0.0100 0.4717
## 60 23.8651 nan 0.0100 0.3730
## 80 17.8533 nan 0.0100 0.1923
## 100 13.6444 nan 0.0100 0.1641
## 120 10.8320 nan 0.0100 0.0984
## 140 8.8153 nan 0.0100 0.0758
## 160 7.3935 nan 0.0100 0.0545
## 180 6.3434 nan 0.0100 0.0200
## 200 5.6228 nan 0.0100 0.0286
## 220 5.0891 nan 0.0100 0.0216
## 240 4.6669 nan 0.0100 0.0089
## 260 4.3635 nan 0.0100 0.0061
## 280 4.1354 nan 0.0100 0.0062
## 300 3.9642 nan 0.0100 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.5047 nan 0.0500 3.7677
## 2 56.0395 nan 0.0500 3.5786
## 3 52.7698 nan 0.0500 3.1785
## 4 49.6318 nan 0.0500 2.8342
## 5 46.7270 nan 0.0500 2.5422
## 6 44.2266 nan 0.0500 1.9435
## 7 41.7435 nan 0.0500 1.9892
## 8 39.8471 nan 0.0500 1.7402
## 9 37.7459 nan 0.0500 1.9216
## 10 36.0801 nan 0.0500 1.7574
## 20 23.1064 nan 0.0500 0.9461
## 40 11.8094 nan 0.0500 0.2641
## 60 7.5838 nan 0.0500 0.0784
## 80 5.7318 nan 0.0500 0.0635
## 100 4.8478 nan 0.0500 0.0088
## 120 4.3847 nan 0.0500 0.0160
## 140 4.1562 nan 0.0500 -0.0013
## 160 4.0257 nan 0.0500 0.0037
## 180 3.9240 nan 0.0500 -0.0007
## 200 3.8486 nan 0.0500 -0.0040
## 220 3.7745 nan 0.0500 -0.0050
## 240 3.7282 nan 0.0500 -0.0155
## 260 3.6757 nan 0.0500 -0.0077
## 280 3.6302 nan 0.0500 -0.0013
## 300 3.5931 nan 0.0500 -0.0148
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.2775 nan 0.0500 3.8648
## 2 55.4966 nan 0.0500 3.3671
## 3 52.3059 nan 0.0500 3.1785
## 4 49.3441 nan 0.0500 2.9452
## 5 46.4254 nan 0.0500 2.5125
## 6 43.9975 nan 0.0500 2.1511
## 7 41.8546 nan 0.0500 2.2623
## 8 39.4682 nan 0.0500 2.0702
## 9 37.4819 nan 0.0500 1.9601
## 10 35.6142 nan 0.0500 1.7867
## 20 23.1449 nan 0.0500 0.9512
## 40 12.1609 nan 0.0500 0.2582
## 60 7.7496 nan 0.0500 0.0660
## 80 5.8031 nan 0.0500 0.0318
## 100 4.8564 nan 0.0500 0.0172
## 120 4.3577 nan 0.0500 0.0121
## 140 4.1329 nan 0.0500 -0.0125
## 160 4.0210 nan 0.0500 -0.0167
## 180 3.8969 nan 0.0500 -0.0128
## 200 3.8363 nan 0.0500 -0.0048
## 220 3.7871 nan 0.0500 -0.0160
## 240 3.7364 nan 0.0500 -0.0233
## 260 3.6812 nan 0.0500 -0.0068
## 280 3.6470 nan 0.0500 -0.0031
## 300 3.6168 nan 0.0500 -0.0170
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.4076 nan 0.0500 3.6972
## 2 55.6875 nan 0.0500 3.6430
## 3 52.4039 nan 0.0500 3.2392
## 4 49.5561 nan 0.0500 2.7492
## 5 46.6213 nan 0.0500 2.6466
## 6 43.9525 nan 0.0500 2.3052
## 7 41.5011 nan 0.0500 2.4400
## 8 39.5083 nan 0.0500 1.7253
## 9 37.6321 nan 0.0500 1.8658
## 10 35.8106 nan 0.0500 1.8050
## 20 22.9937 nan 0.0500 0.8894
## 40 11.9232 nan 0.0500 0.3240
## 60 7.6978 nan 0.0500 0.0949
## 80 5.8189 nan 0.0500 0.0542
## 100 4.9626 nan 0.0500 0.0175
## 120 4.5492 nan 0.0500 -0.0110
## 140 4.3535 nan 0.0500 -0.0063
## 160 4.2300 nan 0.0500 -0.0040
## 180 4.1492 nan 0.0500 -0.0149
## 200 4.0676 nan 0.0500 -0.0089
## 220 4.0177 nan 0.0500 -0.0094
## 240 3.9537 nan 0.0500 -0.0101
## 260 3.9014 nan 0.0500 -0.0129
## 280 3.8561 nan 0.0500 -0.0169
## 300 3.7889 nan 0.0500 -0.0061
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.9732 nan 0.0500 4.8411
## 2 53.3895 nan 0.0500 4.8857
## 3 49.0053 nan 0.0500 4.0397
## 4 45.2499 nan 0.0500 3.7254
## 5 41.6551 nan 0.0500 3.2348
## 6 38.5672 nan 0.0500 3.4127
## 7 35.8895 nan 0.0500 2.4979
## 8 33.6354 nan 0.0500 2.2063
## 9 31.2132 nan 0.0500 2.3220
## 10 29.0091 nan 0.0500 2.2349
## 20 15.1794 nan 0.0500 0.7898
## 40 6.2874 nan 0.0500 0.2063
## 60 4.0876 nan 0.0500 0.0270
## 80 3.4852 nan 0.0500 -0.0080
## 100 3.1574 nan 0.0500 -0.0400
## 120 2.9230 nan 0.0500 -0.0141
## 140 2.7211 nan 0.0500 -0.0176
## 160 2.5266 nan 0.0500 -0.0112
## 180 2.3917 nan 0.0500 -0.0220
## 200 2.2729 nan 0.0500 -0.0162
## 220 2.1668 nan 0.0500 -0.0175
## 240 2.0715 nan 0.0500 -0.0124
## 260 1.9873 nan 0.0500 -0.0093
## 280 1.9056 nan 0.0500 -0.0104
## 300 1.8280 nan 0.0500 -0.0146
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.0690 nan 0.0500 4.7817
## 2 53.4089 nan 0.0500 4.6694
## 3 49.2056 nan 0.0500 4.1396
## 4 45.3607 nan 0.0500 3.6699
## 5 41.7697 nan 0.0500 3.5068
## 6 38.4963 nan 0.0500 2.5788
## 7 35.5744 nan 0.0500 3.0243
## 8 32.8960 nan 0.0500 2.5875
## 9 30.5334 nan 0.0500 2.3667
## 10 28.3511 nan 0.0500 1.9365
## 20 14.7342 nan 0.0500 0.7733
## 40 6.0869 nan 0.0500 0.1687
## 60 4.0585 nan 0.0500 0.0310
## 80 3.5689 nan 0.0500 -0.0117
## 100 3.2529 nan 0.0500 -0.0070
## 120 3.0690 nan 0.0500 -0.0036
## 140 2.9074 nan 0.0500 -0.0097
## 160 2.7547 nan 0.0500 -0.0212
## 180 2.6300 nan 0.0500 -0.0102
## 200 2.5325 nan 0.0500 -0.0127
## 220 2.4488 nan 0.0500 -0.0101
## 240 2.3538 nan 0.0500 -0.0079
## 260 2.2704 nan 0.0500 -0.0106
## 280 2.1898 nan 0.0500 -0.0093
## 300 2.1107 nan 0.0500 -0.0033
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.3457 nan 0.0500 4.5497
## 2 53.7352 nan 0.0500 4.4274
## 3 49.4504 nan 0.0500 4.3940
## 4 45.7361 nan 0.0500 3.8289
## 5 42.3187 nan 0.0500 3.3659
## 6 39.2154 nan 0.0500 2.9067
## 7 36.4085 nan 0.0500 2.8827
## 8 33.6382 nan 0.0500 2.6308
## 9 31.2280 nan 0.0500 2.3426
## 10 29.0276 nan 0.0500 1.8932
## 20 15.2903 nan 0.0500 0.6671
## 40 6.4833 nan 0.0500 0.1260
## 60 4.4829 nan 0.0500 -0.0042
## 80 3.9029 nan 0.0500 -0.0028
## 100 3.6449 nan 0.0500 -0.0123
## 120 3.4567 nan 0.0500 -0.0344
## 140 3.2980 nan 0.0500 -0.0188
## 160 3.1484 nan 0.0500 -0.0196
## 180 3.0318 nan 0.0500 -0.0157
## 200 2.9109 nan 0.0500 -0.0102
## 220 2.8270 nan 0.0500 -0.0177
## 240 2.7271 nan 0.0500 -0.0157
## 260 2.6546 nan 0.0500 -0.0067
## 280 2.5600 nan 0.0500 -0.0280
## 300 2.4856 nan 0.0500 -0.0151
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.6555 nan 0.0500 5.4898
## 2 52.7172 nan 0.0500 4.8280
## 3 48.3536 nan 0.0500 4.1629
## 4 44.3129 nan 0.0500 3.7626
## 5 40.7107 nan 0.0500 3.4519
## 6 37.3951 nan 0.0500 3.6380
## 7 34.2625 nan 0.0500 2.9334
## 8 31.7380 nan 0.0500 2.5877
## 9 29.3210 nan 0.0500 2.3554
## 10 27.1057 nan 0.0500 2.0537
## 20 12.8948 nan 0.0500 0.8838
## 40 4.9318 nan 0.0500 0.1199
## 60 3.3565 nan 0.0500 0.0068
## 80 2.7717 nan 0.0500 -0.0035
## 100 2.4331 nan 0.0500 -0.0127
## 120 2.1977 nan 0.0500 -0.0158
## 140 1.9806 nan 0.0500 -0.0136
## 160 1.8217 nan 0.0500 -0.0191
## 180 1.6777 nan 0.0500 -0.0134
## 200 1.5573 nan 0.0500 -0.0139
## 220 1.4476 nan 0.0500 -0.0154
## 240 1.3314 nan 0.0500 -0.0062
## 260 1.2583 nan 0.0500 -0.0125
## 280 1.1744 nan 0.0500 -0.0130
## 300 1.0928 nan 0.0500 -0.0097
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.7410 nan 0.0500 5.4032
## 2 52.8961 nan 0.0500 4.9490
## 3 48.3202 nan 0.0500 3.9978
## 4 44.2521 nan 0.0500 4.1680
## 5 40.6345 nan 0.0500 3.5487
## 6 37.3365 nan 0.0500 3.1174
## 7 34.3868 nan 0.0500 3.0625
## 8 31.6272 nan 0.0500 2.9383
## 9 29.3122 nan 0.0500 2.4324
## 10 27.0507 nan 0.0500 2.2717
## 20 13.0452 nan 0.0500 0.8046
## 40 5.1410 nan 0.0500 0.1283
## 60 3.6367 nan 0.0500 -0.0036
## 80 3.1242 nan 0.0500 0.0044
## 100 2.8473 nan 0.0500 -0.0204
## 120 2.6180 nan 0.0500 -0.0218
## 140 2.4372 nan 0.0500 -0.0248
## 160 2.2681 nan 0.0500 -0.0075
## 180 2.1267 nan 0.0500 -0.0161
## 200 1.9981 nan 0.0500 -0.0139
## 220 1.8901 nan 0.0500 -0.0152
## 240 1.7892 nan 0.0500 -0.0174
## 260 1.6901 nan 0.0500 -0.0125
## 280 1.6133 nan 0.0500 -0.0183
## 300 1.5347 nan 0.0500 -0.0112
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.7746 nan 0.0500 5.3603
## 2 52.8792 nan 0.0500 4.6078
## 3 48.6105 nan 0.0500 4.2651
## 4 44.5674 nan 0.0500 4.0778
## 5 40.8576 nan 0.0500 3.8866
## 6 37.6271 nan 0.0500 3.2488
## 7 34.4510 nan 0.0500 2.6504
## 8 31.8512 nan 0.0500 2.5021
## 9 29.3322 nan 0.0500 2.2103
## 10 27.3026 nan 0.0500 2.0951
## 20 13.3943 nan 0.0500 0.7328
## 40 5.3889 nan 0.0500 0.0966
## 60 3.9447 nan 0.0500 0.0174
## 80 3.4681 nan 0.0500 -0.0155
## 100 3.1949 nan 0.0500 -0.0243
## 120 2.9657 nan 0.0500 -0.0172
## 140 2.7987 nan 0.0500 -0.0123
## 160 2.6507 nan 0.0500 -0.0213
## 180 2.5054 nan 0.0500 -0.0159
## 200 2.4032 nan 0.0500 -0.0183
## 220 2.2974 nan 0.0500 -0.0198
## 240 2.2013 nan 0.0500 -0.0145
## 260 2.1075 nan 0.0500 -0.0102
## 280 2.0293 nan 0.0500 -0.0103
## 300 1.9488 nan 0.0500 -0.0078
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.0803 nan 0.1000 7.5313
## 2 49.8452 nan 0.1000 6.4820
## 3 44.4064 nan 0.1000 5.3005
## 4 39.5259 nan 0.1000 4.3813
## 5 35.7528 nan 0.1000 3.4928
## 6 32.4886 nan 0.1000 3.1382
## 7 29.2798 nan 0.1000 2.6986
## 8 27.0270 nan 0.1000 2.3115
## 9 24.7436 nan 0.1000 1.6704
## 10 22.8004 nan 0.1000 1.7838
## 20 11.8109 nan 0.1000 0.3964
## 40 5.7387 nan 0.1000 0.0661
## 60 4.4420 nan 0.1000 0.0035
## 80 4.0839 nan 0.1000 -0.0017
## 100 3.9172 nan 0.1000 -0.0297
## 120 3.8046 nan 0.1000 -0.0303
## 140 3.7075 nan 0.1000 -0.0330
## 160 3.6285 nan 0.1000 -0.0092
## 180 3.5439 nan 0.1000 -0.0183
## 200 3.4701 nan 0.1000 -0.0327
## 220 3.3994 nan 0.1000 -0.0170
## 240 3.3283 nan 0.1000 -0.0206
## 260 3.2703 nan 0.1000 -0.0390
## 280 3.2279 nan 0.1000 -0.0189
## 300 3.1826 nan 0.1000 -0.0113
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.2672 nan 0.1000 7.1844
## 2 48.6712 nan 0.1000 6.1719
## 3 43.1145 nan 0.1000 4.9617
## 4 38.7978 nan 0.1000 4.1956
## 5 34.6457 nan 0.1000 3.5746
## 6 31.7696 nan 0.1000 2.8539
## 7 28.6772 nan 0.1000 2.7944
## 8 26.1788 nan 0.1000 2.3845
## 9 24.4291 nan 0.1000 1.6301
## 10 22.5347 nan 0.1000 1.6783
## 20 11.6077 nan 0.1000 0.6700
## 40 5.6273 nan 0.1000 0.0853
## 60 4.3418 nan 0.1000 0.0061
## 80 4.0344 nan 0.1000 0.0034
## 100 3.8896 nan 0.1000 -0.0084
## 120 3.8039 nan 0.1000 -0.0228
## 140 3.7190 nan 0.1000 -0.0123
## 160 3.6533 nan 0.1000 -0.0226
## 180 3.5778 nan 0.1000 -0.0046
## 200 3.4911 nan 0.1000 -0.0160
## 220 3.4363 nan 0.1000 -0.0322
## 240 3.3747 nan 0.1000 -0.0105
## 260 3.3236 nan 0.1000 -0.0145
## 280 3.2648 nan 0.1000 -0.0037
## 300 3.2304 nan 0.1000 -0.0094
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.6990 nan 0.1000 7.2768
## 2 49.6177 nan 0.1000 6.0675
## 3 44.4808 nan 0.1000 4.9048
## 4 39.9892 nan 0.1000 4.3990
## 5 35.7152 nan 0.1000 3.9322
## 6 31.9686 nan 0.1000 2.8026
## 7 28.5617 nan 0.1000 2.7880
## 8 26.1725 nan 0.1000 2.2050
## 9 24.1058 nan 0.1000 2.1288
## 10 22.3283 nan 0.1000 1.6598
## 20 11.9049 nan 0.1000 0.6622
## 40 5.7876 nan 0.1000 0.0838
## 60 4.5613 nan 0.1000 0.0108
## 80 4.2351 nan 0.1000 -0.0362
## 100 4.0641 nan 0.1000 -0.0189
## 120 3.9528 nan 0.1000 -0.0228
## 140 3.8823 nan 0.1000 -0.0520
## 160 3.7857 nan 0.1000 -0.0046
## 180 3.7246 nan 0.1000 -0.0288
## 200 3.6581 nan 0.1000 -0.0231
## 220 3.5823 nan 0.1000 -0.0199
## 240 3.5387 nan 0.1000 -0.0081
## 260 3.4868 nan 0.1000 -0.0212
## 280 3.4314 nan 0.1000 -0.0171
## 300 3.3769 nan 0.1000 -0.0173
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.7290 nan 0.1000 9.9066
## 2 45.6315 nan 0.1000 8.6165
## 3 38.9216 nan 0.1000 5.6129
## 4 33.4303 nan 0.1000 4.8112
## 5 28.5054 nan 0.1000 4.7638
## 6 24.6004 nan 0.1000 3.7476
## 7 21.3019 nan 0.1000 3.2325
## 8 18.4851 nan 0.1000 2.4128
## 9 16.3974 nan 0.1000 1.7965
## 10 14.6864 nan 0.1000 1.7041
## 20 6.0140 nan 0.1000 0.2691
## 40 3.4859 nan 0.1000 -0.0160
## 60 2.8725 nan 0.1000 -0.0297
## 80 2.5612 nan 0.1000 -0.0280
## 100 2.3114 nan 0.1000 -0.0246
## 120 2.1012 nan 0.1000 -0.0331
## 140 1.9231 nan 0.1000 -0.0209
## 160 1.7715 nan 0.1000 -0.0442
## 180 1.6248 nan 0.1000 -0.0098
## 200 1.4951 nan 0.1000 -0.0213
## 220 1.4162 nan 0.1000 -0.0162
## 240 1.3494 nan 0.1000 -0.0130
## 260 1.2600 nan 0.1000 -0.0070
## 280 1.1910 nan 0.1000 -0.0118
## 300 1.1261 nan 0.1000 -0.0153
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.2377 nan 0.1000 9.4861
## 2 45.1783 nan 0.1000 7.9912
## 3 38.9060 nan 0.1000 6.6918
## 4 33.1962 nan 0.1000 5.1613
## 5 28.5414 nan 0.1000 4.1617
## 6 24.7137 nan 0.1000 3.8046
## 7 21.3667 nan 0.1000 3.3157
## 8 18.6283 nan 0.1000 2.3949
## 9 16.3098 nan 0.1000 2.0543
## 10 14.4778 nan 0.1000 1.6397
## 20 6.0105 nan 0.1000 0.3172
## 40 3.5240 nan 0.1000 -0.0144
## 60 3.0721 nan 0.1000 -0.0438
## 80 2.7545 nan 0.1000 -0.0212
## 100 2.5652 nan 0.1000 -0.0618
## 120 2.3642 nan 0.1000 -0.0091
## 140 2.2069 nan 0.1000 -0.0150
## 160 2.0859 nan 0.1000 -0.0312
## 180 1.9946 nan 0.1000 -0.0207
## 200 1.8617 nan 0.1000 -0.0149
## 220 1.7648 nan 0.1000 -0.0204
## 240 1.7029 nan 0.1000 -0.0175
## 260 1.6133 nan 0.1000 -0.0136
## 280 1.5417 nan 0.1000 -0.0215
## 300 1.4541 nan 0.1000 -0.0179
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.2146 nan 0.1000 9.3483
## 2 45.1602 nan 0.1000 8.6411
## 3 38.5428 nan 0.1000 7.0999
## 4 32.8184 nan 0.1000 5.8876
## 5 27.8858 nan 0.1000 4.2108
## 6 24.4795 nan 0.1000 3.6876
## 7 21.7731 nan 0.1000 2.9373
## 8 19.2584 nan 0.1000 2.5896
## 9 17.0369 nan 0.1000 2.2264
## 10 15.2640 nan 0.1000 1.6736
## 20 6.8441 nan 0.1000 0.4335
## 40 3.9777 nan 0.1000 -0.0053
## 60 3.4322 nan 0.1000 -0.0283
## 80 3.1130 nan 0.1000 -0.0325
## 100 2.9361 nan 0.1000 -0.0429
## 120 2.7311 nan 0.1000 -0.0213
## 140 2.5908 nan 0.1000 -0.0211
## 160 2.4328 nan 0.1000 -0.0197
## 180 2.3119 nan 0.1000 -0.0261
## 200 2.1803 nan 0.1000 -0.0191
## 220 2.0844 nan 0.1000 -0.0252
## 240 1.9988 nan 0.1000 -0.0223
## 260 1.9351 nan 0.1000 -0.0349
## 280 1.8526 nan 0.1000 -0.0164
## 300 1.7735 nan 0.1000 -0.0290
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.6829 nan 0.1000 9.4575
## 2 43.9227 nan 0.1000 8.7425
## 3 37.3367 nan 0.1000 6.6163
## 4 31.4983 nan 0.1000 6.0197
## 5 26.6240 nan 0.1000 4.4412
## 6 22.5364 nan 0.1000 3.9279
## 7 19.4797 nan 0.1000 2.8998
## 8 16.7500 nan 0.1000 2.4887
## 9 14.5386 nan 0.1000 2.1272
## 10 12.8489 nan 0.1000 1.5969
## 20 4.8985 nan 0.1000 0.2027
## 40 2.6990 nan 0.1000 0.0051
## 60 2.0987 nan 0.1000 -0.0021
## 80 1.7814 nan 0.1000 -0.0334
## 100 1.5208 nan 0.1000 -0.0424
## 120 1.3232 nan 0.1000 -0.0152
## 140 1.1465 nan 0.1000 -0.0172
## 160 1.0038 nan 0.1000 -0.0061
## 180 0.8904 nan 0.1000 -0.0120
## 200 0.8049 nan 0.1000 -0.0174
## 220 0.7198 nan 0.1000 -0.0115
## 240 0.6522 nan 0.1000 -0.0102
## 260 0.5868 nan 0.1000 -0.0060
## 280 0.5340 nan 0.1000 -0.0056
## 300 0.4855 nan 0.1000 -0.0068
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.4775 nan 0.1000 10.7340
## 2 43.9951 nan 0.1000 9.1355
## 3 37.0396 nan 0.1000 6.6694
## 4 31.3262 nan 0.1000 5.4702
## 5 26.7468 nan 0.1000 4.4205
## 6 22.8081 nan 0.1000 3.5088
## 7 19.6570 nan 0.1000 3.2550
## 8 17.0246 nan 0.1000 2.6802
## 9 14.9335 nan 0.1000 2.1329
## 10 13.0306 nan 0.1000 1.7657
## 20 5.0612 nan 0.1000 0.2234
## 40 3.0807 nan 0.1000 -0.0271
## 60 2.5598 nan 0.1000 -0.0504
## 80 2.2123 nan 0.1000 -0.0337
## 100 1.9912 nan 0.1000 -0.0299
## 120 1.7813 nan 0.1000 -0.0190
## 140 1.5883 nan 0.1000 -0.0245
## 160 1.4691 nan 0.1000 -0.0220
## 180 1.3515 nan 0.1000 -0.0289
## 200 1.2447 nan 0.1000 -0.0226
## 220 1.1206 nan 0.1000 -0.0152
## 240 1.0433 nan 0.1000 -0.0175
## 260 0.9514 nan 0.1000 -0.0061
## 280 0.8781 nan 0.1000 -0.0134
## 300 0.8163 nan 0.1000 -0.0180
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.5734 nan 0.1000 10.2438
## 2 43.7559 nan 0.1000 8.0896
## 3 36.3089 nan 0.1000 7.4155
## 4 30.8100 nan 0.1000 5.3545
## 5 26.1630 nan 0.1000 4.8181
## 6 22.3324 nan 0.1000 4.0317
## 7 19.0919 nan 0.1000 2.8747
## 8 16.7111 nan 0.1000 2.3354
## 9 14.4597 nan 0.1000 1.8226
## 10 12.7555 nan 0.1000 1.9091
## 20 5.3070 nan 0.1000 0.2227
## 40 3.4909 nan 0.1000 -0.0119
## 60 3.0583 nan 0.1000 -0.0497
## 80 2.7795 nan 0.1000 -0.0440
## 100 2.5366 nan 0.1000 -0.0360
## 120 2.2867 nan 0.1000 -0.0200
## 140 2.1206 nan 0.1000 -0.0243
## 160 1.9255 nan 0.1000 -0.0256
## 180 1.7683 nan 0.1000 -0.0373
## 200 1.6424 nan 0.1000 -0.0112
## 220 1.5166 nan 0.1000 -0.0214
## 240 1.4146 nan 0.1000 -0.0242
## 260 1.3280 nan 0.1000 -0.0139
## 280 1.2480 nan 0.1000 -0.0141
## 300 1.1826 nan 0.1000 -0.0210
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.2055 nan 0.0100 0.7649
## 2 58.4764 nan 0.0100 0.6512
## 3 57.7538 nan 0.0100 0.8148
## 4 57.0655 nan 0.0100 0.6700
## 5 56.4288 nan 0.0100 0.6855
## 6 55.7148 nan 0.0100 0.6332
## 7 55.0100 nan 0.0100 0.7080
## 8 54.3008 nan 0.0100 0.7489
## 9 53.6347 nan 0.0100 0.6366
## 10 53.0220 nan 0.0100 0.6009
## 20 47.3155 nan 0.0100 0.5153
## 40 38.1744 nan 0.0100 0.3640
## 60 31.3245 nan 0.0100 0.2694
## 80 26.0187 nan 0.0100 0.2143
## 100 21.9611 nan 0.0100 0.1632
## 120 18.7910 nan 0.0100 0.1068
## 140 16.3227 nan 0.0100 0.0925
## 160 14.3408 nan 0.0100 0.0745
## 180 12.7449 nan 0.0100 0.0695
## 200 11.4512 nan 0.0100 0.0451
## 220 10.4004 nan 0.0100 0.0416
## 240 9.4956 nan 0.0100 0.0405
## 260 8.7639 nan 0.0100 0.0177
## 280 8.1107 nan 0.0100 0.0228
## 300 7.5627 nan 0.0100 0.0236
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.2172 nan 0.0100 0.7959
## 2 58.5000 nan 0.0100 0.7490
## 3 57.7432 nan 0.0100 0.6952
## 4 57.0722 nan 0.0100 0.7074
## 5 56.4215 nan 0.0100 0.5754
## 6 55.7722 nan 0.0100 0.6623
## 7 55.1252 nan 0.0100 0.6396
## 8 54.4376 nan 0.0100 0.6706
## 9 53.8144 nan 0.0100 0.6113
## 10 53.1204 nan 0.0100 0.6285
## 20 47.4312 nan 0.0100 0.5403
## 40 38.1606 nan 0.0100 0.3735
## 60 31.3166 nan 0.0100 0.3128
## 80 26.0916 nan 0.0100 0.2193
## 100 21.9502 nan 0.0100 0.1467
## 120 18.8509 nan 0.0100 0.1279
## 140 16.3704 nan 0.0100 0.0999
## 160 14.3454 nan 0.0100 0.0862
## 180 12.7406 nan 0.0100 0.0481
## 200 11.4441 nan 0.0100 0.0528
## 220 10.3588 nan 0.0100 0.0419
## 240 9.4300 nan 0.0100 0.0376
## 260 8.6832 nan 0.0100 0.0310
## 280 8.0290 nan 0.0100 0.0174
## 300 7.5103 nan 0.0100 0.0190
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.2296 nan 0.0100 0.7425
## 2 58.5102 nan 0.0100 0.7955
## 3 57.8199 nan 0.0100 0.6897
## 4 57.1155 nan 0.0100 0.7518
## 5 56.4129 nan 0.0100 0.6998
## 6 55.6816 nan 0.0100 0.6536
## 7 54.9794 nan 0.0100 0.6278
## 8 54.3355 nan 0.0100 0.6689
## 9 53.6976 nan 0.0100 0.6181
## 10 53.0504 nan 0.0100 0.4874
## 20 47.2064 nan 0.0100 0.5202
## 40 38.1963 nan 0.0100 0.3396
## 60 31.4038 nan 0.0100 0.2507
## 80 26.1567 nan 0.0100 0.2194
## 100 22.0882 nan 0.0100 0.1706
## 120 18.8968 nan 0.0100 0.1227
## 140 16.3688 nan 0.0100 0.0879
## 160 14.4039 nan 0.0100 0.0648
## 180 12.7744 nan 0.0100 0.0663
## 200 11.4732 nan 0.0100 0.0379
## 220 10.3995 nan 0.0100 0.0443
## 240 9.5005 nan 0.0100 0.0298
## 260 8.7740 nan 0.0100 0.0295
## 280 8.1373 nan 0.0100 0.0192
## 300 7.5859 nan 0.0100 0.0206
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.9881 nan 0.0100 0.9252
## 2 57.9850 nan 0.0100 0.8941
## 3 57.0249 nan 0.0100 0.9680
## 4 56.0919 nan 0.0100 1.0039
## 5 55.1750 nan 0.0100 1.0136
## 6 54.2720 nan 0.0100 0.9128
## 7 53.3838 nan 0.0100 0.9110
## 8 52.5337 nan 0.0100 0.8567
## 9 51.7094 nan 0.0100 0.8253
## 10 50.9043 nan 0.0100 0.8165
## 20 43.5253 nan 0.0100 0.5899
## 40 31.9691 nan 0.0100 0.4550
## 60 24.0826 nan 0.0100 0.3469
## 80 18.5107 nan 0.0100 0.2061
## 100 14.5277 nan 0.0100 0.1415
## 120 11.7414 nan 0.0100 0.1090
## 140 9.7198 nan 0.0100 0.0588
## 160 8.2472 nan 0.0100 0.0605
## 180 7.1113 nan 0.0100 0.0409
## 200 6.2665 nan 0.0100 0.0235
## 220 5.6294 nan 0.0100 0.0133
## 240 5.1207 nan 0.0100 0.0140
## 260 4.7755 nan 0.0100 0.0110
## 280 4.4774 nan 0.0100 0.0047
## 300 4.2470 nan 0.0100 0.0015
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.0213 nan 0.0100 1.0300
## 2 58.0681 nan 0.0100 1.0267
## 3 57.1088 nan 0.0100 0.8565
## 4 56.2438 nan 0.0100 0.9469
## 5 55.3183 nan 0.0100 0.9092
## 6 54.4064 nan 0.0100 0.9397
## 7 53.5457 nan 0.0100 0.8244
## 8 52.6850 nan 0.0100 0.7748
## 9 51.8533 nan 0.0100 0.7886
## 10 51.0059 nan 0.0100 0.7634
## 20 43.5978 nan 0.0100 0.6902
## 40 32.1357 nan 0.0100 0.4803
## 60 24.1292 nan 0.0100 0.2968
## 80 18.5851 nan 0.0100 0.2235
## 100 14.5994 nan 0.0100 0.1744
## 120 11.7401 nan 0.0100 0.1218
## 140 9.7248 nan 0.0100 0.1024
## 160 8.2642 nan 0.0100 0.0579
## 180 7.1507 nan 0.0100 0.0379
## 200 6.3387 nan 0.0100 0.0274
## 220 5.7445 nan 0.0100 0.0180
## 240 5.2660 nan 0.0100 0.0187
## 260 4.8890 nan 0.0100 0.0057
## 280 4.6056 nan 0.0100 0.0013
## 300 4.3881 nan 0.0100 0.0047
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.0303 nan 0.0100 0.9225
## 2 58.0235 nan 0.0100 0.9958
## 3 57.0225 nan 0.0100 0.9168
## 4 56.1281 nan 0.0100 0.8586
## 5 55.2039 nan 0.0100 0.9568
## 6 54.2922 nan 0.0100 0.9264
## 7 53.3621 nan 0.0100 0.8998
## 8 52.4992 nan 0.0100 0.7910
## 9 51.6467 nan 0.0100 0.8210
## 10 50.8060 nan 0.0100 0.7185
## 20 43.3211 nan 0.0100 0.6848
## 40 32.0377 nan 0.0100 0.5019
## 60 24.1766 nan 0.0100 0.3266
## 80 18.6001 nan 0.0100 0.2502
## 100 14.7534 nan 0.0100 0.1393
## 120 11.9649 nan 0.0100 0.0966
## 140 9.9451 nan 0.0100 0.0732
## 160 8.4430 nan 0.0100 0.0672
## 180 7.3253 nan 0.0100 0.0401
## 200 6.5429 nan 0.0100 0.0263
## 220 5.9051 nan 0.0100 0.0210
## 240 5.4338 nan 0.0100 0.0170
## 260 5.0842 nan 0.0100 0.0008
## 280 4.8305 nan 0.0100 0.0014
## 300 4.6088 nan 0.0100 0.0043
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.9433 nan 0.0100 1.0394
## 2 57.9199 nan 0.0100 0.9604
## 3 56.9376 nan 0.0100 0.9595
## 4 55.9689 nan 0.0100 0.9725
## 5 55.0147 nan 0.0100 0.9986
## 6 54.0169 nan 0.0100 0.9901
## 7 53.1426 nan 0.0100 0.8499
## 8 52.2802 nan 0.0100 0.9094
## 9 51.4344 nan 0.0100 0.9726
## 10 50.5472 nan 0.0100 0.7647
## 20 42.6206 nan 0.0100 0.6987
## 40 30.7592 nan 0.0100 0.4881
## 60 22.5728 nan 0.0100 0.3365
## 80 16.9905 nan 0.0100 0.2175
## 100 13.0073 nan 0.0100 0.1553
## 120 10.2943 nan 0.0100 0.0872
## 140 8.3465 nan 0.0100 0.0584
## 160 6.9901 nan 0.0100 0.0422
## 180 5.9816 nan 0.0100 0.0316
## 200 5.2494 nan 0.0100 0.0267
## 220 4.7119 nan 0.0100 0.0136
## 240 4.2793 nan 0.0100 0.0059
## 260 3.9677 nan 0.0100 0.0028
## 280 3.7206 nan 0.0100 -0.0032
## 300 3.5405 nan 0.0100 0.0030
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.9771 nan 0.0100 1.0138
## 2 57.9388 nan 0.0100 1.0215
## 3 56.9057 nan 0.0100 0.8507
## 4 55.9144 nan 0.0100 0.8979
## 5 54.9286 nan 0.0100 0.8803
## 6 54.0013 nan 0.0100 0.9204
## 7 53.0771 nan 0.0100 0.9776
## 8 52.1396 nan 0.0100 0.8768
## 9 51.2288 nan 0.0100 0.8359
## 10 50.3323 nan 0.0100 0.7550
## 20 42.4686 nan 0.0100 0.5999
## 40 30.6127 nan 0.0100 0.4739
## 60 22.5577 nan 0.0100 0.3430
## 80 16.9177 nan 0.0100 0.2607
## 100 12.9853 nan 0.0100 0.1590
## 120 10.2636 nan 0.0100 0.0974
## 140 8.3866 nan 0.0100 0.0750
## 160 7.0250 nan 0.0100 0.0379
## 180 6.0291 nan 0.0100 0.0290
## 200 5.3336 nan 0.0100 0.0175
## 220 4.8431 nan 0.0100 0.0155
## 240 4.4717 nan 0.0100 0.0085
## 260 4.1905 nan 0.0100 0.0039
## 280 3.9739 nan 0.0100 0.0021
## 300 3.8022 nan 0.0100 0.0016
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.9336 nan 0.0100 0.9507
## 2 57.8893 nan 0.0100 0.9064
## 3 56.8940 nan 0.0100 1.0292
## 4 55.8967 nan 0.0100 0.8574
## 5 54.9416 nan 0.0100 1.0302
## 6 53.9858 nan 0.0100 1.0444
## 7 53.0169 nan 0.0100 0.7872
## 8 52.1217 nan 0.0100 0.8675
## 9 51.2298 nan 0.0100 0.8214
## 10 50.3466 nan 0.0100 0.9471
## 20 42.3914 nan 0.0100 0.7441
## 40 30.5107 nan 0.0100 0.4414
## 60 22.5680 nan 0.0100 0.3294
## 80 17.0209 nan 0.0100 0.1983
## 100 13.1402 nan 0.0100 0.1475
## 120 10.4200 nan 0.0100 0.1067
## 140 8.5383 nan 0.0100 0.0598
## 160 7.1786 nan 0.0100 0.0489
## 180 6.2518 nan 0.0100 0.0351
## 200 5.5555 nan 0.0100 0.0257
## 220 5.0721 nan 0.0100 0.0197
## 240 4.7175 nan 0.0100 0.0119
## 260 4.4548 nan 0.0100 0.0058
## 280 4.2469 nan 0.0100 0.0015
## 300 4.0950 nan 0.0100 0.0043
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.5437 nan 0.0500 3.7098
## 2 53.0697 nan 0.0500 3.1212
## 3 49.9928 nan 0.0500 2.9125
## 4 47.1722 nan 0.0500 2.7779
## 5 44.9008 nan 0.0500 2.1183
## 6 42.6793 nan 0.0500 2.4575
## 7 40.5084 nan 0.0500 2.1310
## 8 38.3232 nan 0.0500 2.0163
## 9 36.7718 nan 0.0500 1.7894
## 10 34.8427 nan 0.0500 1.7069
## 20 22.3214 nan 0.0500 0.9520
## 40 11.4992 nan 0.0500 0.2846
## 60 7.5983 nan 0.0500 0.1164
## 80 5.7645 nan 0.0500 0.0518
## 100 4.9674 nan 0.0500 0.0154
## 120 4.5245 nan 0.0500 -0.0094
## 140 4.3442 nan 0.0500 -0.0069
## 160 4.2481 nan 0.0500 -0.0163
## 180 4.1385 nan 0.0500 -0.0010
## 200 4.0504 nan 0.0500 -0.0070
## 220 3.9910 nan 0.0500 -0.0034
## 240 3.9255 nan 0.0500 -0.0132
## 260 3.8697 nan 0.0500 0.0007
## 280 3.8276 nan 0.0500 -0.0178
## 300 3.7703 nan 0.0500 -0.0020
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.2519 nan 0.0500 3.6212
## 2 52.5945 nan 0.0500 3.3292
## 3 49.3846 nan 0.0500 2.7744
## 4 46.6029 nan 0.0500 2.5891
## 5 44.1908 nan 0.0500 2.4527
## 6 41.8764 nan 0.0500 2.1739
## 7 39.7937 nan 0.0500 2.1882
## 8 37.9501 nan 0.0500 1.7507
## 9 35.9900 nan 0.0500 1.7515
## 10 34.2726 nan 0.0500 1.7283
## 20 21.3945 nan 0.0500 0.8414
## 40 11.4588 nan 0.0500 0.2039
## 60 7.6137 nan 0.0500 0.0855
## 80 5.8009 nan 0.0500 0.0390
## 100 4.9844 nan 0.0500 0.0261
## 120 4.5739 nan 0.0500 0.0015
## 140 4.4265 nan 0.0500 -0.0165
## 160 4.3170 nan 0.0500 -0.0151
## 180 4.2502 nan 0.0500 -0.0170
## 200 4.1859 nan 0.0500 -0.0081
## 220 4.1156 nan 0.0500 -0.0113
## 240 4.0385 nan 0.0500 -0.0115
## 260 3.9913 nan 0.0500 -0.0214
## 280 3.9414 nan 0.0500 -0.0148
## 300 3.8936 nan 0.0500 -0.0116
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.1189 nan 0.0500 3.8541
## 2 52.6946 nan 0.0500 3.3061
## 3 49.7295 nan 0.0500 2.9384
## 4 47.2452 nan 0.0500 2.2361
## 5 44.5687 nan 0.0500 2.7725
## 6 41.9633 nan 0.0500 2.3470
## 7 39.8315 nan 0.0500 1.8245
## 8 37.7016 nan 0.0500 1.7331
## 9 35.6770 nan 0.0500 1.7630
## 10 33.8346 nan 0.0500 1.6129
## 20 21.3800 nan 0.0500 0.7759
## 40 11.2302 nan 0.0500 0.2535
## 60 7.3073 nan 0.0500 0.0740
## 80 5.6751 nan 0.0500 0.0505
## 100 4.9578 nan 0.0500 0.0039
## 120 4.6405 nan 0.0500 0.0044
## 140 4.4612 nan 0.0500 -0.0060
## 160 4.3504 nan 0.0500 -0.0033
## 180 4.2651 nan 0.0500 -0.0026
## 200 4.1977 nan 0.0500 -0.0060
## 220 4.1340 nan 0.0500 -0.0083
## 240 4.0835 nan 0.0500 -0.0074
## 260 4.0380 nan 0.0500 -0.0078
## 280 3.9891 nan 0.0500 -0.0203
## 300 3.9472 nan 0.0500 -0.0091
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.0031 nan 0.0500 4.7536
## 2 50.6186 nan 0.0500 4.6796
## 3 46.7006 nan 0.0500 3.6753
## 4 43.2462 nan 0.0500 3.6595
## 5 40.0116 nan 0.0500 3.1013
## 6 36.9215 nan 0.0500 3.1171
## 7 34.2902 nan 0.0500 2.6227
## 8 31.7084 nan 0.0500 2.7912
## 9 29.4480 nan 0.0500 1.8641
## 10 27.3565 nan 0.0500 2.0022
## 20 14.2739 nan 0.0500 0.7857
## 40 6.1140 nan 0.0500 0.1950
## 60 4.2708 nan 0.0500 0.0132
## 80 3.6362 nan 0.0500 -0.0039
## 100 3.3328 nan 0.0500 -0.0344
## 120 3.1261 nan 0.0500 -0.0336
## 140 2.9107 nan 0.0500 -0.0101
## 160 2.7323 nan 0.0500 -0.0076
## 180 2.5784 nan 0.0500 -0.0225
## 200 2.4596 nan 0.0500 -0.0221
## 220 2.3346 nan 0.0500 -0.0138
## 240 2.2000 nan 0.0500 -0.0090
## 260 2.1065 nan 0.0500 -0.0087
## 280 2.0111 nan 0.0500 -0.0090
## 300 1.9241 nan 0.0500 -0.0158
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.0596 nan 0.0500 4.8970
## 2 50.6709 nan 0.0500 4.6436
## 3 46.7853 nan 0.0500 3.8941
## 4 43.1507 nan 0.0500 3.7033
## 5 39.8694 nan 0.0500 3.5110
## 6 36.8854 nan 0.0500 3.0157
## 7 34.2227 nan 0.0500 2.3791
## 8 31.7756 nan 0.0500 2.4047
## 9 29.4824 nan 0.0500 2.2494
## 10 27.4525 nan 0.0500 1.8720
## 20 14.4102 nan 0.0500 0.7882
## 40 6.2311 nan 0.0500 0.1794
## 60 4.3526 nan 0.0500 0.0146
## 80 3.7797 nan 0.0500 -0.0049
## 100 3.4830 nan 0.0500 -0.0129
## 120 3.3334 nan 0.0500 -0.0170
## 140 3.1593 nan 0.0500 -0.0156
## 160 3.0000 nan 0.0500 -0.0076
## 180 2.8621 nan 0.0500 -0.0170
## 200 2.7677 nan 0.0500 -0.0134
## 220 2.6649 nan 0.0500 -0.0138
## 240 2.5697 nan 0.0500 -0.0183
## 260 2.4969 nan 0.0500 -0.0226
## 280 2.4077 nan 0.0500 -0.0170
## 300 2.3356 nan 0.0500 -0.0114
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.0833 nan 0.0500 4.8117
## 2 50.6656 nan 0.0500 4.2385
## 3 46.7431 nan 0.0500 3.8701
## 4 42.9569 nan 0.0500 3.5938
## 5 39.7451 nan 0.0500 3.1308
## 6 36.8169 nan 0.0500 3.0587
## 7 34.0997 nan 0.0500 2.5170
## 8 31.5950 nan 0.0500 2.2671
## 9 29.3643 nan 0.0500 2.1708
## 10 27.3067 nan 0.0500 1.9808
## 20 14.4779 nan 0.0500 0.7930
## 40 6.3575 nan 0.0500 0.1344
## 60 4.4701 nan 0.0500 0.0154
## 80 3.9765 nan 0.0500 -0.0194
## 100 3.7571 nan 0.0500 -0.0311
## 120 3.5849 nan 0.0500 -0.0210
## 140 3.4075 nan 0.0500 -0.0103
## 160 3.2624 nan 0.0500 -0.0202
## 180 3.1568 nan 0.0500 -0.0070
## 200 3.0477 nan 0.0500 -0.0056
## 220 2.9617 nan 0.0500 -0.0117
## 240 2.8730 nan 0.0500 -0.0242
## 260 2.7891 nan 0.0500 -0.0168
## 280 2.7071 nan 0.0500 -0.0239
## 300 2.6284 nan 0.0500 -0.0136
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.8492 nan 0.0500 4.5112
## 2 50.0878 nan 0.0500 4.1064
## 3 45.6928 nan 0.0500 3.4714
## 4 41.9608 nan 0.0500 3.0862
## 5 38.5289 nan 0.0500 3.8358
## 6 35.3583 nan 0.0500 3.2631
## 7 32.4365 nan 0.0500 3.0389
## 8 29.9451 nan 0.0500 2.1873
## 9 27.6902 nan 0.0500 2.3201
## 10 25.6727 nan 0.0500 1.8012
## 20 12.6098 nan 0.0500 0.6920
## 40 5.0594 nan 0.0500 0.1118
## 60 3.4778 nan 0.0500 0.0052
## 80 2.9385 nan 0.0500 -0.0361
## 100 2.5817 nan 0.0500 -0.0057
## 120 2.3473 nan 0.0500 -0.0182
## 140 2.1504 nan 0.0500 -0.0235
## 160 1.9689 nan 0.0500 -0.0140
## 180 1.8034 nan 0.0500 -0.0080
## 200 1.6642 nan 0.0500 -0.0089
## 220 1.5486 nan 0.0500 -0.0105
## 240 1.4579 nan 0.0500 -0.0100
## 260 1.3498 nan 0.0500 -0.0079
## 280 1.2615 nan 0.0500 -0.0116
## 300 1.1803 nan 0.0500 -0.0142
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.6374 nan 0.0500 5.1688
## 2 49.8232 nan 0.0500 4.8748
## 3 45.9219 nan 0.0500 3.8021
## 4 42.0953 nan 0.0500 3.7668
## 5 38.6995 nan 0.0500 3.4668
## 6 35.6279 nan 0.0500 3.2604
## 7 32.8248 nan 0.0500 2.7440
## 8 30.2593 nan 0.0500 2.3873
## 9 27.8219 nan 0.0500 2.0921
## 10 25.7640 nan 0.0500 2.2734
## 20 12.8661 nan 0.0500 0.8644
## 40 5.3059 nan 0.0500 0.0879
## 60 3.8528 nan 0.0500 0.0108
## 80 3.3234 nan 0.0500 -0.0208
## 100 3.0574 nan 0.0500 -0.0395
## 120 2.8417 nan 0.0500 -0.0215
## 140 2.6546 nan 0.0500 -0.0343
## 160 2.4573 nan 0.0500 -0.0258
## 180 2.2995 nan 0.0500 -0.0151
## 200 2.1688 nan 0.0500 -0.0176
## 220 2.0336 nan 0.0500 -0.0122
## 240 1.9357 nan 0.0500 -0.0201
## 260 1.8412 nan 0.0500 -0.0160
## 280 1.7303 nan 0.0500 -0.0092
## 300 1.6556 nan 0.0500 -0.0152
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.0788 nan 0.0500 4.7070
## 2 50.7048 nan 0.0500 4.2124
## 3 46.5665 nan 0.0500 3.8875
## 4 42.6249 nan 0.0500 3.9539
## 5 39.2931 nan 0.0500 3.2314
## 6 36.2105 nan 0.0500 2.8375
## 7 33.1810 nan 0.0500 2.7027
## 8 30.8120 nan 0.0500 2.3923
## 9 28.5429 nan 0.0500 2.4052
## 10 26.1687 nan 0.0500 1.9534
## 20 13.0188 nan 0.0500 0.8343
## 40 5.5594 nan 0.0500 0.1089
## 60 4.1702 nan 0.0500 -0.0218
## 80 3.7304 nan 0.0500 -0.0354
## 100 3.4427 nan 0.0500 -0.0104
## 120 3.2040 nan 0.0500 -0.0182
## 140 3.0432 nan 0.0500 -0.0142
## 160 2.8790 nan 0.0500 -0.0158
## 180 2.7583 nan 0.0500 -0.0244
## 200 2.6451 nan 0.0500 -0.0167
## 220 2.5285 nan 0.0500 -0.0052
## 240 2.4306 nan 0.0500 -0.0070
## 260 2.3596 nan 0.0500 -0.0139
## 280 2.2816 nan 0.0500 -0.0134
## 300 2.1927 nan 0.0500 -0.0092
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.5281 nan 0.1000 7.2479
## 2 47.8149 nan 0.1000 5.5422
## 3 42.4283 nan 0.1000 4.6154
## 4 37.8311 nan 0.1000 3.8128
## 5 34.1432 nan 0.1000 3.2938
## 6 30.9308 nan 0.1000 3.0563
## 7 28.1967 nan 0.1000 2.8445
## 8 25.7157 nan 0.1000 2.2590
## 9 23.5325 nan 0.1000 2.2328
## 10 21.5697 nan 0.1000 1.5910
## 20 11.4103 nan 0.1000 0.4502
## 40 5.8908 nan 0.1000 0.0649
## 60 4.7470 nan 0.1000 0.0244
## 80 4.4876 nan 0.1000 -0.0195
## 100 4.2819 nan 0.1000 -0.0303
## 120 4.1210 nan 0.1000 -0.0486
## 140 4.0010 nan 0.1000 -0.0140
## 160 3.9178 nan 0.1000 -0.0038
## 180 3.8191 nan 0.1000 -0.0033
## 200 3.7323 nan 0.1000 -0.0178
## 220 3.6466 nan 0.1000 -0.0273
## 240 3.5952 nan 0.1000 -0.0385
## 260 3.5568 nan 0.1000 -0.0101
## 280 3.4850 nan 0.1000 -0.0117
## 300 3.4171 nan 0.1000 -0.0290
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.4688 nan 0.1000 6.8355
## 2 46.7668 nan 0.1000 5.6443
## 3 42.0435 nan 0.1000 3.7916
## 4 37.6993 nan 0.1000 4.1359
## 5 33.9098 nan 0.1000 3.6237
## 6 30.6766 nan 0.1000 3.0740
## 7 28.0555 nan 0.1000 2.6132
## 8 25.7755 nan 0.1000 2.3319
## 9 23.5610 nan 0.1000 2.1597
## 10 21.7994 nan 0.1000 1.4807
## 20 11.2909 nan 0.1000 0.4523
## 40 5.7716 nan 0.1000 0.1114
## 60 4.6718 nan 0.1000 0.0014
## 80 4.4266 nan 0.1000 -0.0225
## 100 4.2606 nan 0.1000 -0.0082
## 120 4.1296 nan 0.1000 -0.0291
## 140 4.0137 nan 0.1000 -0.0248
## 160 3.9415 nan 0.1000 -0.0137
## 180 3.8712 nan 0.1000 -0.0206
## 200 3.7952 nan 0.1000 -0.0216
## 220 3.7414 nan 0.1000 -0.0157
## 240 3.6685 nan 0.1000 -0.0029
## 260 3.6078 nan 0.1000 -0.0007
## 280 3.5374 nan 0.1000 -0.0101
## 300 3.4830 nan 0.1000 -0.0273
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.5100 nan 0.1000 7.3293
## 2 47.0078 nan 0.1000 4.4970
## 3 41.5680 nan 0.1000 5.0181
## 4 37.8943 nan 0.1000 2.9780
## 5 34.0442 nan 0.1000 3.8521
## 6 30.6512 nan 0.1000 3.3069
## 7 28.0318 nan 0.1000 2.5961
## 8 25.5893 nan 0.1000 2.3818
## 9 23.6862 nan 0.1000 1.9288
## 10 21.7993 nan 0.1000 1.6415
## 20 11.1761 nan 0.1000 0.4423
## 40 5.8680 nan 0.1000 0.0417
## 60 4.8751 nan 0.1000 0.0218
## 80 4.6029 nan 0.1000 -0.0001
## 100 4.4036 nan 0.1000 -0.0353
## 120 4.3228 nan 0.1000 -0.0226
## 140 4.2011 nan 0.1000 -0.0193
## 160 4.0845 nan 0.1000 -0.0070
## 180 4.0007 nan 0.1000 -0.0113
## 200 3.9276 nan 0.1000 -0.0088
## 220 3.8578 nan 0.1000 -0.0142
## 240 3.7646 nan 0.1000 -0.0167
## 260 3.7279 nan 0.1000 -0.0070
## 280 3.6619 nan 0.1000 -0.0199
## 300 3.6180 nan 0.1000 -0.0356
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.5569 nan 0.1000 8.9914
## 2 42.9705 nan 0.1000 8.5212
## 3 36.2586 nan 0.1000 6.2291
## 4 30.9603 nan 0.1000 4.9982
## 5 26.5420 nan 0.1000 3.8534
## 6 23.2380 nan 0.1000 3.4850
## 7 20.2497 nan 0.1000 2.9528
## 8 17.7105 nan 0.1000 2.4129
## 9 15.7135 nan 0.1000 1.7885
## 10 13.9571 nan 0.1000 1.5724
## 20 6.1086 nan 0.1000 0.2061
## 40 3.6734 nan 0.1000 -0.0460
## 60 3.1664 nan 0.1000 -0.0425
## 80 2.7902 nan 0.1000 -0.0377
## 100 2.4625 nan 0.1000 -0.0293
## 120 2.2336 nan 0.1000 -0.0352
## 140 2.0254 nan 0.1000 -0.0328
## 160 1.8481 nan 0.1000 -0.0248
## 180 1.7119 nan 0.1000 -0.0222
## 200 1.5902 nan 0.1000 -0.0182
## 220 1.4940 nan 0.1000 -0.0340
## 240 1.3882 nan 0.1000 -0.0247
## 260 1.3119 nan 0.1000 -0.0136
## 280 1.2483 nan 0.1000 -0.0200
## 300 1.1720 nan 0.1000 -0.0120
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.3462 nan 0.1000 10.0493
## 2 42.8392 nan 0.1000 7.9173
## 3 36.4507 nan 0.1000 6.1877
## 4 31.0251 nan 0.1000 5.7378
## 5 26.7595 nan 0.1000 4.2033
## 6 23.2069 nan 0.1000 3.7299
## 7 20.3183 nan 0.1000 2.2430
## 8 17.9982 nan 0.1000 2.5113
## 9 15.9327 nan 0.1000 1.9216
## 10 13.9819 nan 0.1000 1.9531
## 20 6.0913 nan 0.1000 0.3516
## 40 3.8748 nan 0.1000 -0.0575
## 60 3.3743 nan 0.1000 -0.0592
## 80 3.0458 nan 0.1000 -0.0129
## 100 2.8265 nan 0.1000 -0.0392
## 120 2.6353 nan 0.1000 -0.0324
## 140 2.4789 nan 0.1000 -0.0281
## 160 2.3258 nan 0.1000 -0.0157
## 180 2.1810 nan 0.1000 -0.0343
## 200 2.0515 nan 0.1000 -0.0331
## 220 1.9500 nan 0.1000 -0.0103
## 240 1.8550 nan 0.1000 -0.0134
## 260 1.7561 nan 0.1000 -0.0267
## 280 1.6364 nan 0.1000 -0.0169
## 300 1.5617 nan 0.1000 -0.0173
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.6671 nan 0.1000 9.9589
## 2 42.7561 nan 0.1000 7.9993
## 3 36.2836 nan 0.1000 6.1796
## 4 31.1824 nan 0.1000 5.3339
## 5 26.8244 nan 0.1000 3.8962
## 6 23.0563 nan 0.1000 3.4314
## 7 20.3664 nan 0.1000 2.1878
## 8 17.9298 nan 0.1000 2.2807
## 9 15.8261 nan 0.1000 2.1278
## 10 13.9396 nan 0.1000 1.6152
## 20 6.2854 nan 0.1000 0.3411
## 40 4.0349 nan 0.1000 -0.0027
## 60 3.5577 nan 0.1000 -0.0394
## 80 3.3283 nan 0.1000 -0.0447
## 100 3.1081 nan 0.1000 -0.0310
## 120 2.9438 nan 0.1000 -0.0208
## 140 2.7790 nan 0.1000 -0.0228
## 160 2.6710 nan 0.1000 -0.0521
## 180 2.5232 nan 0.1000 -0.0441
## 200 2.4025 nan 0.1000 -0.0267
## 220 2.3108 nan 0.1000 -0.0054
## 240 2.2322 nan 0.1000 -0.0334
## 260 2.1119 nan 0.1000 -0.0159
## 280 2.0132 nan 0.1000 -0.0230
## 300 1.9296 nan 0.1000 -0.0186
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.0617 nan 0.1000 10.7192
## 2 41.9580 nan 0.1000 7.5755
## 3 35.6701 nan 0.1000 6.1662
## 4 30.5153 nan 0.1000 4.6316
## 5 25.9751 nan 0.1000 4.5299
## 6 22.3096 nan 0.1000 3.3097
## 7 18.9979 nan 0.1000 2.9207
## 8 16.4853 nan 0.1000 2.2905
## 9 14.1806 nan 0.1000 2.1583
## 10 12.5830 nan 0.1000 1.6320
## 20 5.1973 nan 0.1000 0.1760
## 40 3.1049 nan 0.1000 -0.0290
## 60 2.4424 nan 0.1000 -0.0539
## 80 2.0284 nan 0.1000 -0.0248
## 100 1.7115 nan 0.1000 -0.0156
## 120 1.4794 nan 0.1000 -0.0230
## 140 1.2833 nan 0.1000 -0.0102
## 160 1.1413 nan 0.1000 -0.0221
## 180 1.0078 nan 0.1000 -0.0131
## 200 0.8872 nan 0.1000 -0.0111
## 220 0.8038 nan 0.1000 -0.0132
## 240 0.7118 nan 0.1000 -0.0111
## 260 0.6464 nan 0.1000 -0.0119
## 280 0.5772 nan 0.1000 -0.0092
## 300 0.5311 nan 0.1000 -0.0098
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.3042 nan 0.1000 10.2468
## 2 42.2105 nan 0.1000 7.9157
## 3 35.3382 nan 0.1000 6.5465
## 4 29.6980 nan 0.1000 5.4504
## 5 25.4972 nan 0.1000 4.1565
## 6 22.0284 nan 0.1000 3.3164
## 7 18.9599 nan 0.1000 2.7861
## 8 16.4990 nan 0.1000 2.4939
## 9 14.3586 nan 0.1000 2.1044
## 10 12.4830 nan 0.1000 1.6987
## 20 5.0833 nan 0.1000 0.1616
## 40 3.3004 nan 0.1000 -0.0661
## 60 2.7950 nan 0.1000 -0.0718
## 80 2.4609 nan 0.1000 -0.0481
## 100 2.1921 nan 0.1000 -0.0158
## 120 1.9888 nan 0.1000 -0.0496
## 140 1.8065 nan 0.1000 -0.0207
## 160 1.6749 nan 0.1000 -0.0121
## 180 1.5159 nan 0.1000 -0.0223
## 200 1.3948 nan 0.1000 -0.0241
## 220 1.3063 nan 0.1000 -0.0282
## 240 1.1998 nan 0.1000 -0.0258
## 260 1.1139 nan 0.1000 -0.0114
## 280 1.0310 nan 0.1000 -0.0140
## 300 0.9670 nan 0.1000 -0.0196
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.2072 nan 0.1000 10.7663
## 2 42.1874 nan 0.1000 8.2551
## 3 35.5826 nan 0.1000 6.8193
## 4 30.0732 nan 0.1000 5.0494
## 5 25.3116 nan 0.1000 3.8473
## 6 21.6925 nan 0.1000 3.3290
## 7 18.6747 nan 0.1000 2.8151
## 8 16.1471 nan 0.1000 2.4604
## 9 13.8891 nan 0.1000 2.2895
## 10 12.1598 nan 0.1000 1.6660
## 20 5.2250 nan 0.1000 0.2167
## 40 3.6210 nan 0.1000 -0.0366
## 60 3.1802 nan 0.1000 -0.0213
## 80 2.8731 nan 0.1000 -0.0365
## 100 2.6212 nan 0.1000 -0.0467
## 120 2.4340 nan 0.1000 -0.0312
## 140 2.2588 nan 0.1000 -0.0316
## 160 2.0799 nan 0.1000 -0.0425
## 180 1.9716 nan 0.1000 -0.0153
## 200 1.8239 nan 0.1000 -0.0262
## 220 1.7260 nan 0.1000 -0.0316
## 240 1.6443 nan 0.1000 -0.0459
## 260 1.5428 nan 0.1000 -0.0245
## 280 1.4481 nan 0.1000 -0.0252
## 300 1.3750 nan 0.1000 -0.0236
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.7660 nan 0.0100 0.7927
## 2 59.9760 nan 0.0100 0.7477
## 3 59.2460 nan 0.0100 0.7661
## 4 58.5057 nan 0.0100 0.7125
## 5 57.7785 nan 0.0100 0.6982
## 6 57.0185 nan 0.0100 0.6674
## 7 56.2991 nan 0.0100 0.6669
## 8 55.6880 nan 0.0100 0.6322
## 9 55.0246 nan 0.0100 0.6155
## 10 54.3761 nan 0.0100 0.6360
## 20 48.7411 nan 0.0100 0.4891
## 40 39.4304 nan 0.0100 0.3750
## 60 32.2577 nan 0.0100 0.3153
## 80 26.7796 nan 0.0100 0.2220
## 100 22.7368 nan 0.0100 0.1573
## 120 19.5698 nan 0.0100 0.1236
## 140 17.0398 nan 0.0100 0.0929
## 160 15.0214 nan 0.0100 0.0786
## 180 13.4291 nan 0.0100 0.0611
## 200 12.0654 nan 0.0100 0.0583
## 220 10.9251 nan 0.0100 0.0325
## 240 9.9583 nan 0.0100 0.0414
## 260 9.1621 nan 0.0100 0.0304
## 280 8.4568 nan 0.0100 0.0284
## 300 7.8666 nan 0.0100 0.0126
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.7870 nan 0.0100 0.7521
## 2 59.9866 nan 0.0100 0.7324
## 3 59.2573 nan 0.0100 0.7847
## 4 58.5858 nan 0.0100 0.7678
## 5 57.8359 nan 0.0100 0.7086
## 6 57.1980 nan 0.0100 0.6928
## 7 56.5639 nan 0.0100 0.6651
## 8 55.8827 nan 0.0100 0.6671
## 9 55.2338 nan 0.0100 0.6655
## 10 54.5715 nan 0.0100 0.6858
## 20 48.6301 nan 0.0100 0.5175
## 40 39.4264 nan 0.0100 0.3907
## 60 32.2260 nan 0.0100 0.2737
## 80 26.8744 nan 0.0100 0.2050
## 100 22.8219 nan 0.0100 0.1605
## 120 19.6324 nan 0.0100 0.1127
## 140 17.0758 nan 0.0100 0.1062
## 160 15.0522 nan 0.0100 0.0783
## 180 13.4349 nan 0.0100 0.0576
## 200 12.0865 nan 0.0100 0.0231
## 220 10.9397 nan 0.0100 0.0408
## 240 9.9785 nan 0.0100 0.0353
## 260 9.1596 nan 0.0100 0.0242
## 280 8.5025 nan 0.0100 0.0225
## 300 7.9023 nan 0.0100 0.0253
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.8056 nan 0.0100 0.7570
## 2 59.9650 nan 0.0100 0.7216
## 3 59.2376 nan 0.0100 0.6620
## 4 58.5428 nan 0.0100 0.7071
## 5 57.8593 nan 0.0100 0.6982
## 6 57.1008 nan 0.0100 0.7085
## 7 56.4487 nan 0.0100 0.6525
## 8 55.7938 nan 0.0100 0.6292
## 9 55.1709 nan 0.0100 0.6243
## 10 54.4801 nan 0.0100 0.6401
## 20 48.7284 nan 0.0100 0.5626
## 40 39.4472 nan 0.0100 0.3906
## 60 32.5514 nan 0.0100 0.2997
## 80 27.1401 nan 0.0100 0.2148
## 100 22.9807 nan 0.0100 0.1464
## 120 19.8094 nan 0.0100 0.1218
## 140 17.2751 nan 0.0100 0.1043
## 160 15.2594 nan 0.0100 0.0786
## 180 13.5752 nan 0.0100 0.0567
## 200 12.1654 nan 0.0100 0.0432
## 220 11.0535 nan 0.0100 0.0412
## 240 10.1118 nan 0.0100 0.0383
## 260 9.3100 nan 0.0100 0.0279
## 280 8.6686 nan 0.0100 0.0156
## 300 8.0720 nan 0.0100 0.0240
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.6128 nan 0.0100 1.0022
## 2 59.6328 nan 0.0100 0.9715
## 3 58.6518 nan 0.0100 0.8832
## 4 57.6960 nan 0.0100 0.9816
## 5 56.8173 nan 0.0100 0.9529
## 6 55.9287 nan 0.0100 0.7488
## 7 55.0415 nan 0.0100 0.8416
## 8 54.1264 nan 0.0100 0.8460
## 9 53.2894 nan 0.0100 0.8406
## 10 52.4748 nan 0.0100 0.9753
## 20 44.7402 nan 0.0100 0.7383
## 40 33.0959 nan 0.0100 0.4822
## 60 24.9887 nan 0.0100 0.3277
## 80 19.1918 nan 0.0100 0.2214
## 100 15.1476 nan 0.0100 0.1284
## 120 12.2353 nan 0.0100 0.0843
## 140 10.0538 nan 0.0100 0.0946
## 160 8.4775 nan 0.0100 0.0641
## 180 7.2453 nan 0.0100 0.0427
## 200 6.3517 nan 0.0100 0.0266
## 220 5.6713 nan 0.0100 0.0245
## 240 5.1686 nan 0.0100 0.0153
## 260 4.7650 nan 0.0100 0.0105
## 280 4.4646 nan 0.0100 0.0067
## 300 4.2264 nan 0.0100 0.0029
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.5423 nan 0.0100 1.0307
## 2 59.5733 nan 0.0100 0.8742
## 3 58.5631 nan 0.0100 0.9935
## 4 57.6165 nan 0.0100 0.9033
## 5 56.7044 nan 0.0100 0.9568
## 6 55.7388 nan 0.0100 0.9210
## 7 54.8304 nan 0.0100 0.8119
## 8 53.9934 nan 0.0100 0.9096
## 9 53.1347 nan 0.0100 0.8593
## 10 52.3063 nan 0.0100 0.8568
## 20 44.8563 nan 0.0100 0.7231
## 40 32.9925 nan 0.0100 0.5209
## 60 24.9360 nan 0.0100 0.3016
## 80 19.1590 nan 0.0100 0.2231
## 100 15.1536 nan 0.0100 0.1545
## 120 12.2133 nan 0.0100 0.1124
## 140 10.0810 nan 0.0100 0.0798
## 160 8.4968 nan 0.0100 0.0561
## 180 7.3302 nan 0.0100 0.0431
## 200 6.4736 nan 0.0100 0.0340
## 220 5.8020 nan 0.0100 0.0259
## 240 5.2909 nan 0.0100 0.0189
## 260 4.9316 nan 0.0100 0.0145
## 280 4.6331 nan 0.0100 0.0074
## 300 4.3976 nan 0.0100 0.0014
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.5356 nan 0.0100 1.0743
## 2 59.5432 nan 0.0100 0.9617
## 3 58.6154 nan 0.0100 0.8984
## 4 57.6770 nan 0.0100 0.8789
## 5 56.7191 nan 0.0100 0.8860
## 6 55.8204 nan 0.0100 0.8711
## 7 54.8951 nan 0.0100 0.8866
## 8 54.0114 nan 0.0100 0.8630
## 9 53.1628 nan 0.0100 0.8181
## 10 52.3114 nan 0.0100 0.7424
## 20 44.6154 nan 0.0100 0.7551
## 40 33.0485 nan 0.0100 0.4651
## 60 24.9674 nan 0.0100 0.3414
## 80 19.2844 nan 0.0100 0.2349
## 100 15.2722 nan 0.0100 0.1593
## 120 12.3756 nan 0.0100 0.0879
## 140 10.2981 nan 0.0100 0.0777
## 160 8.7805 nan 0.0100 0.0578
## 180 7.6221 nan 0.0100 0.0443
## 200 6.7545 nan 0.0100 0.0296
## 220 6.1149 nan 0.0100 0.0198
## 240 5.6238 nan 0.0100 0.0135
## 260 5.2662 nan 0.0100 0.0097
## 280 4.9687 nan 0.0100 0.0059
## 300 4.7149 nan 0.0100 0.0010
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.5280 nan 0.0100 1.0017
## 2 59.4748 nan 0.0100 0.9913
## 3 58.3938 nan 0.0100 0.9527
## 4 57.4358 nan 0.0100 1.0419
## 5 56.3800 nan 0.0100 0.9905
## 6 55.4186 nan 0.0100 0.9519
## 7 54.4388 nan 0.0100 0.9300
## 8 53.5170 nan 0.0100 0.8389
## 9 52.5851 nan 0.0100 0.8512
## 10 51.7152 nan 0.0100 0.9048
## 20 43.6651 nan 0.0100 0.7197
## 40 31.5091 nan 0.0100 0.4966
## 60 23.0466 nan 0.0100 0.3513
## 80 17.2732 nan 0.0100 0.2402
## 100 13.2369 nan 0.0100 0.1814
## 120 10.3737 nan 0.0100 0.0978
## 140 8.3112 nan 0.0100 0.0581
## 160 6.9222 nan 0.0100 0.0510
## 180 5.9136 nan 0.0100 0.0315
## 200 5.1603 nan 0.0100 0.0218
## 220 4.6269 nan 0.0100 0.0170
## 240 4.1995 nan 0.0100 0.0151
## 260 3.8660 nan 0.0100 0.0042
## 280 3.6156 nan 0.0100 -0.0011
## 300 3.4135 nan 0.0100 0.0016
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.4604 nan 0.0100 0.9130
## 2 59.4163 nan 0.0100 1.0636
## 3 58.3742 nan 0.0100 0.9256
## 4 57.3522 nan 0.0100 0.9604
## 5 56.3652 nan 0.0100 1.0048
## 6 55.4290 nan 0.0100 0.8571
## 7 54.4483 nan 0.0100 0.9713
## 8 53.5343 nan 0.0100 0.8623
## 9 52.6204 nan 0.0100 0.8903
## 10 51.7416 nan 0.0100 0.9246
## 20 43.7414 nan 0.0100 0.7379
## 40 31.4345 nan 0.0100 0.4721
## 60 23.0673 nan 0.0100 0.3276
## 80 17.1852 nan 0.0100 0.1959
## 100 13.2600 nan 0.0100 0.1481
## 120 10.4380 nan 0.0100 0.1210
## 140 8.5035 nan 0.0100 0.0816
## 160 7.1124 nan 0.0100 0.0507
## 180 6.0979 nan 0.0100 0.0300
## 200 5.3779 nan 0.0100 0.0110
## 220 4.8405 nan 0.0100 0.0163
## 240 4.4333 nan 0.0100 0.0071
## 260 4.1369 nan 0.0100 0.0021
## 280 3.9018 nan 0.0100 -0.0008
## 300 3.7187 nan 0.0100 -0.0017
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.4956 nan 0.0100 1.1134
## 2 59.4575 nan 0.0100 0.9308
## 3 58.4299 nan 0.0100 0.9929
## 4 57.4287 nan 0.0100 0.8407
## 5 56.4436 nan 0.0100 1.0395
## 6 55.4575 nan 0.0100 0.9833
## 7 54.5460 nan 0.0100 0.9485
## 8 53.6878 nan 0.0100 0.9528
## 9 52.7938 nan 0.0100 0.9172
## 10 51.9040 nan 0.0100 0.8074
## 20 43.8624 nan 0.0100 0.7445
## 40 31.8083 nan 0.0100 0.4275
## 60 23.4099 nan 0.0100 0.2895
## 80 17.5890 nan 0.0100 0.2429
## 100 13.6808 nan 0.0100 0.1471
## 120 10.8663 nan 0.0100 0.0977
## 140 8.8851 nan 0.0100 0.0708
## 160 7.4761 nan 0.0100 0.0551
## 180 6.4844 nan 0.0100 0.0395
## 200 5.7690 nan 0.0100 0.0272
## 220 5.2517 nan 0.0100 0.0132
## 240 4.8505 nan 0.0100 0.0116
## 260 4.5769 nan 0.0100 0.0029
## 280 4.3490 nan 0.0100 0.0079
## 300 4.1635 nan 0.0100 -0.0003
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.7015 nan 0.0500 3.4982
## 2 54.2418 nan 0.0500 3.1776
## 3 51.0148 nan 0.0500 2.7234
## 4 48.1848 nan 0.0500 2.8085
## 5 45.9234 nan 0.0500 2.3217
## 6 43.5831 nan 0.0500 2.4684
## 7 41.5490 nan 0.0500 1.7567
## 8 39.5428 nan 0.0500 1.8231
## 9 37.6141 nan 0.0500 2.1494
## 10 35.7790 nan 0.0500 1.7716
## 20 22.9533 nan 0.0500 0.9134
## 40 12.2589 nan 0.0500 0.2651
## 60 8.1055 nan 0.0500 0.1218
## 80 6.0937 nan 0.0500 0.0217
## 100 5.2074 nan 0.0500 -0.0067
## 120 4.7190 nan 0.0500 -0.0083
## 140 4.4785 nan 0.0500 -0.0069
## 160 4.3330 nan 0.0500 -0.0086
## 180 4.2576 nan 0.0500 -0.0184
## 200 4.1562 nan 0.0500 -0.0269
## 220 4.0912 nan 0.0500 -0.0132
## 240 4.0271 nan 0.0500 -0.0104
## 260 3.9630 nan 0.0500 -0.0184
## 280 3.9027 nan 0.0500 -0.0027
## 300 3.8374 nan 0.0500 -0.0206
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.5794 nan 0.0500 3.7992
## 2 54.3230 nan 0.0500 3.2092
## 3 50.9122 nan 0.0500 3.2266
## 4 48.0505 nan 0.0500 2.5112
## 5 45.4481 nan 0.0500 2.2672
## 6 43.0324 nan 0.0500 2.2788
## 7 40.7323 nan 0.0500 1.9265
## 8 38.7387 nan 0.0500 1.9601
## 9 36.7217 nan 0.0500 1.9000
## 10 34.8995 nan 0.0500 1.6411
## 20 22.4937 nan 0.0500 0.9089
## 40 11.8917 nan 0.0500 0.2149
## 60 7.7633 nan 0.0500 0.1225
## 80 5.9714 nan 0.0500 0.0120
## 100 5.0798 nan 0.0500 -0.0101
## 120 4.7007 nan 0.0500 0.0081
## 140 4.4883 nan 0.0500 -0.0114
## 160 4.3417 nan 0.0500 -0.0147
## 180 4.2465 nan 0.0500 -0.0115
## 200 4.1704 nan 0.0500 -0.0351
## 220 4.1005 nan 0.0500 -0.0115
## 240 4.0413 nan 0.0500 -0.0055
## 260 3.9864 nan 0.0500 -0.0031
## 280 3.9366 nan 0.0500 -0.0141
## 300 3.8887 nan 0.0500 -0.0218
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.7586 nan 0.0500 3.5394
## 2 54.2993 nan 0.0500 3.7989
## 3 50.9941 nan 0.0500 2.9821
## 4 48.2152 nan 0.0500 2.4799
## 5 45.5350 nan 0.0500 2.4753
## 6 43.1732 nan 0.0500 2.1965
## 7 40.9369 nan 0.0500 2.2222
## 8 38.7347 nan 0.0500 2.0183
## 9 37.0822 nan 0.0500 1.7347
## 10 35.2522 nan 0.0500 1.6259
## 20 22.4820 nan 0.0500 0.8752
## 40 11.9974 nan 0.0500 0.2520
## 60 8.0188 nan 0.0500 0.0830
## 80 6.2073 nan 0.0500 0.0574
## 100 5.3636 nan 0.0500 0.0242
## 120 4.9926 nan 0.0500 0.0054
## 140 4.7710 nan 0.0500 -0.0054
## 160 4.6372 nan 0.0500 -0.0150
## 180 4.4956 nan 0.0500 -0.0032
## 200 4.4152 nan 0.0500 -0.0095
## 220 4.3268 nan 0.0500 -0.0298
## 240 4.2491 nan 0.0500 -0.0035
## 260 4.1822 nan 0.0500 -0.0180
## 280 4.1274 nan 0.0500 -0.0079
## 300 4.0693 nan 0.0500 -0.0078
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.7125 nan 0.0500 4.8834
## 2 52.0471 nan 0.0500 4.5408
## 3 48.0224 nan 0.0500 3.6153
## 4 44.5774 nan 0.0500 3.6362
## 5 41.1548 nan 0.0500 3.6088
## 6 38.1180 nan 0.0500 3.0381
## 7 35.3822 nan 0.0500 2.8435
## 8 32.8657 nan 0.0500 2.2436
## 9 30.5260 nan 0.0500 2.3342
## 10 28.4272 nan 0.0500 1.9290
## 20 14.7456 nan 0.0500 0.7583
## 40 6.2283 nan 0.0500 0.1508
## 60 4.2450 nan 0.0500 0.0093
## 80 3.5616 nan 0.0500 -0.0029
## 100 3.1795 nan 0.0500 -0.0228
## 120 2.9053 nan 0.0500 -0.0146
## 140 2.6717 nan 0.0500 -0.0094
## 160 2.5057 nan 0.0500 -0.0022
## 180 2.3555 nan 0.0500 -0.0145
## 200 2.2091 nan 0.0500 -0.0110
## 220 2.0794 nan 0.0500 -0.0099
## 240 1.9680 nan 0.0500 -0.0180
## 260 1.8718 nan 0.0500 -0.0193
## 280 1.7793 nan 0.0500 -0.0071
## 300 1.7130 nan 0.0500 -0.0149
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.7490 nan 0.0500 4.6916
## 2 52.3650 nan 0.0500 4.6628
## 3 48.4174 nan 0.0500 3.9909
## 4 44.7935 nan 0.0500 3.4565
## 5 41.4009 nan 0.0500 3.0197
## 6 38.2488 nan 0.0500 2.7600
## 7 35.3945 nan 0.0500 2.7726
## 8 32.6785 nan 0.0500 2.6216
## 9 30.4299 nan 0.0500 2.2168
## 10 28.2481 nan 0.0500 2.0930
## 20 14.7523 nan 0.0500 0.8113
## 40 6.4072 nan 0.0500 0.1383
## 60 4.3777 nan 0.0500 -0.0053
## 80 3.7216 nan 0.0500 -0.0066
## 100 3.3925 nan 0.0500 -0.0137
## 120 3.1470 nan 0.0500 -0.0176
## 140 2.9600 nan 0.0500 -0.0195
## 160 2.8231 nan 0.0500 -0.0119
## 180 2.6784 nan 0.0500 -0.0159
## 200 2.5609 nan 0.0500 -0.0174
## 220 2.4551 nan 0.0500 -0.0076
## 240 2.3622 nan 0.0500 -0.0143
## 260 2.2791 nan 0.0500 -0.0149
## 280 2.1983 nan 0.0500 -0.0199
## 300 2.1106 nan 0.0500 -0.0137
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.8504 nan 0.0500 5.5487
## 2 52.3049 nan 0.0500 4.3477
## 3 48.1635 nan 0.0500 4.0110
## 4 44.4085 nan 0.0500 3.7169
## 5 41.2354 nan 0.0500 3.0825
## 6 38.0796 nan 0.0500 2.9684
## 7 35.1033 nan 0.0500 2.6028
## 8 32.6581 nan 0.0500 2.4002
## 9 30.3600 nan 0.0500 2.2336
## 10 28.4114 nan 0.0500 2.1210
## 20 15.0378 nan 0.0500 0.7062
## 40 6.8280 nan 0.0500 0.1544
## 60 4.8615 nan 0.0500 0.0122
## 80 4.2513 nan 0.0500 -0.0209
## 100 3.8814 nan 0.0500 -0.0027
## 120 3.6592 nan 0.0500 -0.0246
## 140 3.4690 nan 0.0500 -0.0126
## 160 3.2878 nan 0.0500 0.0015
## 180 3.1510 nan 0.0500 -0.0087
## 200 3.0242 nan 0.0500 -0.0176
## 220 2.9191 nan 0.0500 -0.0308
## 240 2.8278 nan 0.0500 -0.0117
## 260 2.7560 nan 0.0500 -0.0069
## 280 2.6777 nan 0.0500 -0.0183
## 300 2.6141 nan 0.0500 -0.0129
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.2638 nan 0.0500 4.9113
## 2 51.4327 nan 0.0500 5.1917
## 3 47.1931 nan 0.0500 4.5336
## 4 43.3367 nan 0.0500 3.5749
## 5 39.7113 nan 0.0500 3.3623
## 6 36.5267 nan 0.0500 3.0600
## 7 33.6145 nan 0.0500 2.9483
## 8 30.9439 nan 0.0500 2.4926
## 9 28.6520 nan 0.0500 2.3665
## 10 26.3901 nan 0.0500 2.0501
## 20 12.7373 nan 0.0500 0.6948
## 40 5.1322 nan 0.0500 0.1296
## 60 3.3519 nan 0.0500 -0.0029
## 80 2.7871 nan 0.0500 -0.0279
## 100 2.4228 nan 0.0500 -0.0154
## 120 2.1971 nan 0.0500 -0.0238
## 140 1.9685 nan 0.0500 -0.0051
## 160 1.8083 nan 0.0500 -0.0032
## 180 1.6591 nan 0.0500 -0.0111
## 200 1.5165 nan 0.0500 -0.0174
## 220 1.3881 nan 0.0500 -0.0046
## 240 1.2850 nan 0.0500 -0.0086
## 260 1.1870 nan 0.0500 -0.0076
## 280 1.0969 nan 0.0500 -0.0124
## 300 1.0232 nan 0.0500 -0.0125
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.3257 nan 0.0500 4.9082
## 2 51.7223 nan 0.0500 4.1841
## 3 47.2697 nan 0.0500 4.0280
## 4 43.3561 nan 0.0500 3.7546
## 5 39.8132 nan 0.0500 3.4950
## 6 36.6496 nan 0.0500 3.0146
## 7 33.7619 nan 0.0500 2.8775
## 8 31.0063 nan 0.0500 2.6138
## 9 28.8234 nan 0.0500 2.1678
## 10 26.6081 nan 0.0500 2.1545
## 20 13.1800 nan 0.0500 0.7379
## 40 5.3308 nan 0.0500 0.1217
## 60 3.7864 nan 0.0500 -0.0058
## 80 3.2331 nan 0.0500 -0.0191
## 100 2.9085 nan 0.0500 -0.0242
## 120 2.6610 nan 0.0500 -0.0175
## 140 2.4777 nan 0.0500 -0.0077
## 160 2.3397 nan 0.0500 -0.0256
## 180 2.2029 nan 0.0500 -0.0218
## 200 2.0719 nan 0.0500 -0.0258
## 220 1.9302 nan 0.0500 -0.0066
## 240 1.8126 nan 0.0500 -0.0084
## 260 1.7167 nan 0.0500 -0.0163
## 280 1.6456 nan 0.0500 -0.0303
## 300 1.5770 nan 0.0500 -0.0154
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.6400 nan 0.0500 4.3550
## 2 52.0289 nan 0.0500 4.9811
## 3 47.9229 nan 0.0500 3.8790
## 4 44.0696 nan 0.0500 4.2387
## 5 40.5396 nan 0.0500 3.5277
## 6 37.4770 nan 0.0500 2.9603
## 7 34.4191 nan 0.0500 2.9682
## 8 31.6216 nan 0.0500 2.6704
## 9 29.2256 nan 0.0500 2.2461
## 10 27.0938 nan 0.0500 2.3321
## 20 13.3959 nan 0.0500 0.6797
## 40 5.6904 nan 0.0500 0.1482
## 60 4.1340 nan 0.0500 0.0103
## 80 3.6248 nan 0.0500 -0.0299
## 100 3.3372 nan 0.0500 -0.0193
## 120 3.0990 nan 0.0500 -0.0128
## 140 2.9222 nan 0.0500 -0.0148
## 160 2.7660 nan 0.0500 -0.0206
## 180 2.6155 nan 0.0500 -0.0071
## 200 2.4911 nan 0.0500 -0.0093
## 220 2.3656 nan 0.0500 -0.0193
## 240 2.2562 nan 0.0500 -0.0124
## 260 2.1599 nan 0.0500 -0.0316
## 280 2.0814 nan 0.0500 -0.0160
## 300 1.9923 nan 0.0500 -0.0011
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.2847 nan 0.1000 7.5454
## 2 48.6925 nan 0.1000 5.3311
## 3 44.1672 nan 0.1000 5.0422
## 4 39.6491 nan 0.1000 4.4760
## 5 35.7243 nan 0.1000 3.6096
## 6 32.6440 nan 0.1000 2.8981
## 7 29.4232 nan 0.1000 2.9324
## 8 27.0610 nan 0.1000 2.1609
## 9 24.9332 nan 0.1000 1.9953
## 10 22.8532 nan 0.1000 1.8774
## 20 11.9913 nan 0.1000 0.5764
## 40 5.9687 nan 0.1000 0.0648
## 60 4.6823 nan 0.1000 0.0043
## 80 4.3640 nan 0.1000 -0.0267
## 100 4.1831 nan 0.1000 -0.0480
## 120 4.0730 nan 0.1000 -0.0253
## 140 3.9445 nan 0.1000 -0.0369
## 160 3.8502 nan 0.1000 0.0005
## 180 3.7419 nan 0.1000 -0.0084
## 200 3.6585 nan 0.1000 -0.0078
## 220 3.5719 nan 0.1000 -0.0146
## 240 3.4860 nan 0.1000 -0.0248
## 260 3.4062 nan 0.1000 -0.0156
## 280 3.3333 nan 0.1000 -0.0101
## 300 3.2440 nan 0.1000 -0.0143
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.4360 nan 0.1000 7.1786
## 2 48.2436 nan 0.1000 5.9560
## 3 42.9664 nan 0.1000 4.7632
## 4 38.5017 nan 0.1000 3.8203
## 5 34.9034 nan 0.1000 3.6767
## 6 31.8816 nan 0.1000 3.1260
## 7 28.7641 nan 0.1000 2.6304
## 8 26.3670 nan 0.1000 2.1303
## 9 24.0570 nan 0.1000 1.9654
## 10 21.9052 nan 0.1000 1.5430
## 20 11.6009 nan 0.1000 0.4593
## 40 6.0548 nan 0.1000 0.0773
## 60 4.7745 nan 0.1000 -0.0061
## 80 4.4454 nan 0.1000 -0.0107
## 100 4.2585 nan 0.1000 -0.0184
## 120 4.1151 nan 0.1000 -0.0049
## 140 4.0180 nan 0.1000 -0.0325
## 160 3.9088 nan 0.1000 -0.0065
## 180 3.8272 nan 0.1000 -0.0172
## 200 3.7435 nan 0.1000 -0.0121
## 220 3.6725 nan 0.1000 -0.0367
## 240 3.5888 nan 0.1000 -0.0115
## 260 3.5253 nan 0.1000 -0.0218
## 280 3.4719 nan 0.1000 -0.0266
## 300 3.4208 nan 0.1000 -0.0139
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.9771 nan 0.1000 7.0895
## 2 47.9560 nan 0.1000 5.8764
## 3 43.0336 nan 0.1000 4.8825
## 4 38.8824 nan 0.1000 3.9261
## 5 35.1650 nan 0.1000 3.3880
## 6 31.7645 nan 0.1000 3.4813
## 7 28.9339 nan 0.1000 2.4061
## 8 26.3186 nan 0.1000 2.1991
## 9 24.0556 nan 0.1000 1.9092
## 10 22.2098 nan 0.1000 1.9348
## 20 12.0798 nan 0.1000 0.5723
## 40 6.2028 nan 0.1000 0.1175
## 60 5.0612 nan 0.1000 0.0113
## 80 4.7066 nan 0.1000 -0.0083
## 100 4.5007 nan 0.1000 -0.0166
## 120 4.3527 nan 0.1000 -0.0188
## 140 4.2346 nan 0.1000 -0.0182
## 160 4.1149 nan 0.1000 -0.0065
## 180 4.0134 nan 0.1000 -0.0158
## 200 3.9291 nan 0.1000 -0.0067
## 220 3.8515 nan 0.1000 -0.0139
## 240 3.7660 nan 0.1000 -0.0163
## 260 3.7190 nan 0.1000 -0.0038
## 280 3.6664 nan 0.1000 -0.0144
## 300 3.5887 nan 0.1000 -0.0125
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.7831 nan 0.1000 9.1923
## 2 43.9110 nan 0.1000 8.8358
## 3 37.2493 nan 0.1000 6.8920
## 4 31.9451 nan 0.1000 5.2992
## 5 28.0059 nan 0.1000 3.8268
## 6 24.2273 nan 0.1000 3.6710
## 7 21.1069 nan 0.1000 2.6253
## 8 18.8089 nan 0.1000 2.3356
## 9 16.6477 nan 0.1000 2.1193
## 10 15.1227 nan 0.1000 1.4087
## 20 6.4822 nan 0.1000 0.2781
## 40 3.6473 nan 0.1000 0.0004
## 60 3.0684 nan 0.1000 -0.0373
## 80 2.6830 nan 0.1000 -0.0257
## 100 2.3901 nan 0.1000 -0.0758
## 120 2.1295 nan 0.1000 -0.0589
## 140 1.9358 nan 0.1000 -0.0120
## 160 1.7685 nan 0.1000 -0.0249
## 180 1.5799 nan 0.1000 -0.0172
## 200 1.4666 nan 0.1000 -0.0247
## 220 1.3486 nan 0.1000 -0.0160
## 240 1.2328 nan 0.1000 -0.0135
## 260 1.1368 nan 0.1000 -0.0163
## 280 1.0465 nan 0.1000 -0.0074
## 300 0.9787 nan 0.1000 -0.0133
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.4299 nan 0.1000 9.3723
## 2 44.2958 nan 0.1000 8.8230
## 3 37.6021 nan 0.1000 6.3263
## 4 32.4734 nan 0.1000 5.4821
## 5 27.9151 nan 0.1000 4.2789
## 6 24.1962 nan 0.1000 3.4308
## 7 21.0915 nan 0.1000 3.0697
## 8 18.3793 nan 0.1000 2.0839
## 9 16.3224 nan 0.1000 1.7534
## 10 14.6533 nan 0.1000 1.7295
## 20 6.3795 nan 0.1000 0.2951
## 40 3.7911 nan 0.1000 -0.0214
## 60 3.2749 nan 0.1000 -0.0337
## 80 2.9521 nan 0.1000 -0.0505
## 100 2.6855 nan 0.1000 -0.0448
## 120 2.4726 nan 0.1000 -0.0195
## 140 2.3036 nan 0.1000 -0.0284
## 160 2.1852 nan 0.1000 -0.0343
## 180 2.0308 nan 0.1000 -0.0130
## 200 1.9137 nan 0.1000 -0.0482
## 220 1.7811 nan 0.1000 -0.0243
## 240 1.7000 nan 0.1000 -0.0366
## 260 1.6306 nan 0.1000 -0.0191
## 280 1.5485 nan 0.1000 -0.0163
## 300 1.4767 nan 0.1000 -0.0282
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.6957 nan 0.1000 10.2754
## 2 43.9888 nan 0.1000 5.8718
## 3 37.2893 nan 0.1000 6.3956
## 4 31.7999 nan 0.1000 5.0527
## 5 27.3631 nan 0.1000 4.6902
## 6 23.6505 nan 0.1000 3.0164
## 7 20.5370 nan 0.1000 2.7276
## 8 17.9467 nan 0.1000 2.4883
## 9 16.2444 nan 0.1000 1.7189
## 10 14.4171 nan 0.1000 1.7024
## 20 6.3703 nan 0.1000 0.2859
## 40 4.0622 nan 0.1000 -0.0069
## 60 3.4892 nan 0.1000 -0.0649
## 80 3.1642 nan 0.1000 -0.0277
## 100 2.9496 nan 0.1000 -0.0575
## 120 2.7454 nan 0.1000 -0.0347
## 140 2.6094 nan 0.1000 -0.0205
## 160 2.4530 nan 0.1000 -0.0320
## 180 2.3304 nan 0.1000 -0.0473
## 200 2.1898 nan 0.1000 -0.0050
## 220 2.0959 nan 0.1000 -0.0191
## 240 1.9861 nan 0.1000 -0.0119
## 260 1.8927 nan 0.1000 -0.0231
## 280 1.8116 nan 0.1000 -0.0159
## 300 1.7470 nan 0.1000 -0.0239
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.5094 nan 0.1000 10.0884
## 2 42.6263 nan 0.1000 7.9548
## 3 36.0184 nan 0.1000 7.0857
## 4 30.6992 nan 0.1000 5.2137
## 5 26.1656 nan 0.1000 4.0115
## 6 22.3809 nan 0.1000 3.1315
## 7 19.2653 nan 0.1000 2.8562
## 8 16.5477 nan 0.1000 2.4638
## 9 14.4104 nan 0.1000 1.9341
## 10 12.7724 nan 0.1000 1.4613
## 20 4.9430 nan 0.1000 0.3141
## 40 2.8922 nan 0.1000 -0.0826
## 60 2.2484 nan 0.1000 -0.0216
## 80 1.8591 nan 0.1000 -0.0362
## 100 1.5492 nan 0.1000 -0.0420
## 120 1.3282 nan 0.1000 -0.0318
## 140 1.1419 nan 0.1000 -0.0140
## 160 0.9678 nan 0.1000 -0.0162
## 180 0.8545 nan 0.1000 -0.0158
## 200 0.7652 nan 0.1000 -0.0185
## 220 0.6638 nan 0.1000 -0.0193
## 240 0.5770 nan 0.1000 -0.0133
## 260 0.5156 nan 0.1000 -0.0055
## 280 0.4601 nan 0.1000 -0.0049
## 300 0.4192 nan 0.1000 -0.0075
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.5295 nan 0.1000 10.7635
## 2 42.9585 nan 0.1000 8.2234
## 3 36.4052 nan 0.1000 6.6474
## 4 31.1299 nan 0.1000 4.5835
## 5 26.2568 nan 0.1000 4.3051
## 6 22.3920 nan 0.1000 3.5802
## 7 19.3733 nan 0.1000 2.9887
## 8 16.7005 nan 0.1000 2.3146
## 9 14.6994 nan 0.1000 1.9832
## 10 13.0350 nan 0.1000 1.5186
## 20 5.2289 nan 0.1000 0.1753
## 40 3.0827 nan 0.1000 -0.0080
## 60 2.6379 nan 0.1000 -0.0207
## 80 2.2997 nan 0.1000 -0.0350
## 100 2.0528 nan 0.1000 -0.0432
## 120 1.8291 nan 0.1000 -0.0384
## 140 1.6362 nan 0.1000 -0.0106
## 160 1.4719 nan 0.1000 -0.0286
## 180 1.3424 nan 0.1000 -0.0254
## 200 1.2191 nan 0.1000 -0.0260
## 220 1.1263 nan 0.1000 -0.0248
## 240 1.0218 nan 0.1000 -0.0117
## 260 0.9483 nan 0.1000 -0.0149
## 280 0.8664 nan 0.1000 -0.0131
## 300 0.8018 nan 0.1000 -0.0111
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.5351 nan 0.1000 9.6900
## 2 43.1175 nan 0.1000 8.7664
## 3 36.3583 nan 0.1000 6.5872
## 4 30.3095 nan 0.1000 5.8680
## 5 25.5715 nan 0.1000 4.2419
## 6 22.0715 nan 0.1000 3.6646
## 7 19.0115 nan 0.1000 3.2653
## 8 16.6682 nan 0.1000 2.4997
## 9 14.6179 nan 0.1000 1.9630
## 10 12.9433 nan 0.1000 1.5236
## 20 5.6848 nan 0.1000 0.2340
## 40 3.7360 nan 0.1000 -0.0338
## 60 3.2756 nan 0.1000 -0.0596
## 80 2.9915 nan 0.1000 -0.0369
## 100 2.7077 nan 0.1000 -0.0549
## 120 2.4969 nan 0.1000 -0.0376
## 140 2.3198 nan 0.1000 -0.0333
## 160 2.1127 nan 0.1000 -0.0418
## 180 1.9531 nan 0.1000 -0.0190
## 200 1.8175 nan 0.1000 -0.0179
## 220 1.6914 nan 0.1000 -0.0225
## 240 1.5730 nan 0.1000 -0.0135
## 260 1.4840 nan 0.1000 -0.0196
## 280 1.3939 nan 0.1000 -0.0142
## 300 1.3282 nan 0.1000 -0.0197
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.2782 nan 0.0100 0.7547
## 2 60.4911 nan 0.0100 0.7609
## 3 59.8464 nan 0.0100 0.6957
## 4 59.1555 nan 0.0100 0.7994
## 5 58.4687 nan 0.0100 0.7077
## 6 57.7765 nan 0.0100 0.6926
## 7 57.0277 nan 0.0100 0.6271
## 8 56.3670 nan 0.0100 0.6321
## 9 55.6581 nan 0.0100 0.6682
## 10 55.0170 nan 0.0100 0.6144
## 20 49.3185 nan 0.0100 0.5477
## 40 39.8234 nan 0.0100 0.3762
## 60 32.7438 nan 0.0100 0.3144
## 80 27.4280 nan 0.0100 0.2301
## 100 23.1311 nan 0.0100 0.1549
## 120 19.8965 nan 0.0100 0.1442
## 140 17.3439 nan 0.0100 0.0960
## 160 15.3253 nan 0.0100 0.0917
## 180 13.5949 nan 0.0100 0.0590
## 200 12.2595 nan 0.0100 0.0276
## 220 11.0968 nan 0.0100 0.0404
## 240 10.1079 nan 0.0100 0.0339
## 260 9.2429 nan 0.0100 0.0225
## 280 8.5532 nan 0.0100 0.0331
## 300 7.9692 nan 0.0100 0.0274
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.2032 nan 0.0100 0.7791
## 2 60.4498 nan 0.0100 0.7671
## 3 59.7171 nan 0.0100 0.6749
## 4 59.1124 nan 0.0100 0.6010
## 5 58.3623 nan 0.0100 0.6742
## 6 57.6567 nan 0.0100 0.6628
## 7 56.9899 nan 0.0100 0.7006
## 8 56.2986 nan 0.0100 0.6062
## 9 55.5947 nan 0.0100 0.6568
## 10 54.9510 nan 0.0100 0.6569
## 20 49.0352 nan 0.0100 0.5077
## 40 39.5332 nan 0.0100 0.4050
## 60 32.5387 nan 0.0100 0.3055
## 80 27.1126 nan 0.0100 0.1973
## 100 23.0153 nan 0.0100 0.1716
## 120 19.8695 nan 0.0100 0.1226
## 140 17.2766 nan 0.0100 0.1083
## 160 15.2465 nan 0.0100 0.0742
## 180 13.5743 nan 0.0100 0.0529
## 200 12.1343 nan 0.0100 0.0602
## 220 10.9952 nan 0.0100 0.0422
## 240 10.0011 nan 0.0100 0.0347
## 260 9.1952 nan 0.0100 0.0314
## 280 8.4795 nan 0.0100 0.0161
## 300 7.8864 nan 0.0100 0.0070
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.1751 nan 0.0100 0.7565
## 2 60.4646 nan 0.0100 0.7551
## 3 59.7129 nan 0.0100 0.7414
## 4 58.9753 nan 0.0100 0.6788
## 5 58.2159 nan 0.0100 0.6441
## 6 57.4814 nan 0.0100 0.6932
## 7 56.7929 nan 0.0100 0.6312
## 8 56.0925 nan 0.0100 0.6759
## 9 55.4329 nan 0.0100 0.6614
## 10 54.7291 nan 0.0100 0.6077
## 20 48.7355 nan 0.0100 0.5156
## 40 39.4375 nan 0.0100 0.3859
## 60 32.4370 nan 0.0100 0.2478
## 80 27.0441 nan 0.0100 0.2366
## 100 22.8406 nan 0.0100 0.1715
## 120 19.6887 nan 0.0100 0.1411
## 140 17.1510 nan 0.0100 0.0925
## 160 15.1039 nan 0.0100 0.0590
## 180 13.4811 nan 0.0100 0.0641
## 200 12.1224 nan 0.0100 0.0511
## 220 10.9726 nan 0.0100 0.0379
## 240 10.0197 nan 0.0100 0.0383
## 260 9.2190 nan 0.0100 0.0167
## 280 8.5204 nan 0.0100 0.0263
## 300 7.9473 nan 0.0100 0.0253
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.9673 nan 0.0100 1.1014
## 2 60.0113 nan 0.0100 1.0332
## 3 59.0835 nan 0.0100 0.9142
## 4 58.1361 nan 0.0100 0.9753
## 5 57.2506 nan 0.0100 0.9082
## 6 56.2395 nan 0.0100 0.8998
## 7 55.2943 nan 0.0100 0.8753
## 8 54.3674 nan 0.0100 0.8091
## 9 53.4745 nan 0.0100 0.8212
## 10 52.6147 nan 0.0100 0.8496
## 20 44.9554 nan 0.0100 0.7143
## 40 33.1898 nan 0.0100 0.4872
## 60 25.0167 nan 0.0100 0.3252
## 80 19.1779 nan 0.0100 0.2385
## 100 15.0999 nan 0.0100 0.1559
## 120 12.2186 nan 0.0100 0.1355
## 140 10.0133 nan 0.0100 0.0732
## 160 8.4532 nan 0.0100 0.0629
## 180 7.2764 nan 0.0100 0.0459
## 200 6.4265 nan 0.0100 0.0390
## 220 5.7298 nan 0.0100 0.0152
## 240 5.1990 nan 0.0100 0.0128
## 260 4.8165 nan 0.0100 0.0051
## 280 4.5014 nan 0.0100 0.0048
## 300 4.2759 nan 0.0100 0.0027
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.9618 nan 0.0100 0.9912
## 2 59.9884 nan 0.0100 0.9463
## 3 58.9741 nan 0.0100 1.0465
## 4 57.9778 nan 0.0100 0.9638
## 5 57.0541 nan 0.0100 0.9541
## 6 56.1177 nan 0.0100 0.9155
## 7 55.1801 nan 0.0100 0.9345
## 8 54.3021 nan 0.0100 0.8906
## 9 53.4836 nan 0.0100 0.8987
## 10 52.6431 nan 0.0100 0.8870
## 20 44.8019 nan 0.0100 0.7496
## 40 33.1322 nan 0.0100 0.4327
## 60 24.8941 nan 0.0100 0.3239
## 80 19.2709 nan 0.0100 0.2609
## 100 15.1453 nan 0.0100 0.1614
## 120 12.2285 nan 0.0100 0.0973
## 140 10.1777 nan 0.0100 0.0776
## 160 8.5915 nan 0.0100 0.0510
## 180 7.4301 nan 0.0100 0.0347
## 200 6.5481 nan 0.0100 0.0367
## 220 5.8612 nan 0.0100 0.0246
## 240 5.3379 nan 0.0100 0.0139
## 260 4.9536 nan 0.0100 0.0146
## 280 4.6432 nan 0.0100 0.0056
## 300 4.4226 nan 0.0100 0.0011
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.9484 nan 0.0100 0.9642
## 2 59.9405 nan 0.0100 1.1000
## 3 58.9755 nan 0.0100 0.9454
## 4 58.0619 nan 0.0100 0.9695
## 5 57.1092 nan 0.0100 0.9359
## 6 56.1662 nan 0.0100 0.7976
## 7 55.2474 nan 0.0100 0.8715
## 8 54.3634 nan 0.0100 0.7532
## 9 53.5102 nan 0.0100 0.9008
## 10 52.6693 nan 0.0100 0.8708
## 20 44.8179 nan 0.0100 0.6580
## 40 33.1220 nan 0.0100 0.4176
## 60 24.9934 nan 0.0100 0.3294
## 80 19.2953 nan 0.0100 0.2322
## 100 15.2673 nan 0.0100 0.1688
## 120 12.3992 nan 0.0100 0.1088
## 140 10.2690 nan 0.0100 0.0764
## 160 8.7538 nan 0.0100 0.0511
## 180 7.5496 nan 0.0100 0.0419
## 200 6.6881 nan 0.0100 0.0216
## 220 6.0339 nan 0.0100 0.0204
## 240 5.5379 nan 0.0100 0.0164
## 260 5.1856 nan 0.0100 0.0136
## 280 4.8905 nan 0.0100 0.0114
## 300 4.6574 nan 0.0100 0.0027
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.9066 nan 0.0100 1.0793
## 2 59.8637 nan 0.0100 1.1211
## 3 58.7984 nan 0.0100 1.0466
## 4 57.7997 nan 0.0100 0.9490
## 5 56.8108 nan 0.0100 0.8728
## 6 55.8808 nan 0.0100 0.9942
## 7 54.9091 nan 0.0100 0.8586
## 8 53.9576 nan 0.0100 0.9741
## 9 52.9840 nan 0.0100 0.9174
## 10 52.0550 nan 0.0100 0.9862
## 20 43.9723 nan 0.0100 0.7460
## 40 31.6908 nan 0.0100 0.4533
## 60 23.2891 nan 0.0100 0.3550
## 80 17.4293 nan 0.0100 0.2488
## 100 13.2933 nan 0.0100 0.1942
## 120 10.4793 nan 0.0100 0.1200
## 140 8.5063 nan 0.0100 0.0668
## 160 7.0616 nan 0.0100 0.0342
## 180 6.0231 nan 0.0100 0.0283
## 200 5.2383 nan 0.0100 0.0169
## 220 4.6363 nan 0.0100 0.0148
## 240 4.2156 nan 0.0100 0.0078
## 260 3.8933 nan 0.0100 0.0077
## 280 3.6321 nan 0.0100 0.0050
## 300 3.4228 nan 0.0100 0.0033
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.8542 nan 0.0100 1.0554
## 2 59.7785 nan 0.0100 0.9839
## 3 58.7497 nan 0.0100 0.9876
## 4 57.7135 nan 0.0100 1.1079
## 5 56.6976 nan 0.0100 0.9991
## 6 55.6967 nan 0.0100 0.9325
## 7 54.7520 nan 0.0100 0.9363
## 8 53.8409 nan 0.0100 0.9579
## 9 52.9597 nan 0.0100 0.9174
## 10 52.0546 nan 0.0100 0.8634
## 20 44.0498 nan 0.0100 0.7668
## 40 31.6326 nan 0.0100 0.4931
## 60 23.2712 nan 0.0100 0.3170
## 80 17.4886 nan 0.0100 0.2272
## 100 13.4487 nan 0.0100 0.1517
## 120 10.5255 nan 0.0100 0.1185
## 140 8.5247 nan 0.0100 0.0850
## 160 7.0966 nan 0.0100 0.0504
## 180 6.0861 nan 0.0100 0.0232
## 200 5.3776 nan 0.0100 0.0210
## 220 4.8300 nan 0.0100 0.0014
## 240 4.4625 nan 0.0100 0.0132
## 260 4.1338 nan 0.0100 0.0102
## 280 3.9148 nan 0.0100 0.0011
## 300 3.7296 nan 0.0100 0.0021
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.8955 nan 0.0100 1.0620
## 2 59.8036 nan 0.0100 0.9926
## 3 58.8117 nan 0.0100 1.1072
## 4 57.8520 nan 0.0100 0.9819
## 5 56.8469 nan 0.0100 0.8601
## 6 55.8695 nan 0.0100 1.0273
## 7 54.9189 nan 0.0100 0.8766
## 8 54.0025 nan 0.0100 0.9191
## 9 53.1187 nan 0.0100 0.8876
## 10 52.1809 nan 0.0100 0.9734
## 20 44.2258 nan 0.0100 0.7035
## 40 31.9399 nan 0.0100 0.4012
## 60 23.5770 nan 0.0100 0.3014
## 80 17.6580 nan 0.0100 0.2422
## 100 13.6456 nan 0.0100 0.1330
## 120 10.8694 nan 0.0100 0.1122
## 140 8.8884 nan 0.0100 0.0833
## 160 7.5281 nan 0.0100 0.0528
## 180 6.5305 nan 0.0100 0.0355
## 200 5.8123 nan 0.0100 0.0167
## 220 5.2650 nan 0.0100 0.0144
## 240 4.8759 nan 0.0100 0.0103
## 260 4.5771 nan 0.0100 0.0071
## 280 4.3504 nan 0.0100 0.0051
## 300 4.1737 nan 0.0100 0.0022
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.2006 nan 0.0500 3.7901
## 2 55.2052 nan 0.0500 3.2358
## 3 51.9308 nan 0.0500 3.1021
## 4 48.9960 nan 0.0500 2.8302
## 5 46.3911 nan 0.0500 2.6234
## 6 43.8995 nan 0.0500 2.2379
## 7 41.5016 nan 0.0500 2.1831
## 8 39.6352 nan 0.0500 1.9675
## 9 37.7607 nan 0.0500 2.0399
## 10 36.0613 nan 0.0500 1.7732
## 20 23.1878 nan 0.0500 0.9456
## 40 11.8278 nan 0.0500 0.2510
## 60 7.7737 nan 0.0500 0.1266
## 80 5.9591 nan 0.0500 0.0387
## 100 5.0405 nan 0.0500 0.0181
## 120 4.5846 nan 0.0500 -0.0046
## 140 4.3993 nan 0.0500 -0.0107
## 160 4.2509 nan 0.0500 -0.0193
## 180 4.1496 nan 0.0500 0.0017
## 200 4.0758 nan 0.0500 -0.0348
## 220 3.9887 nan 0.0500 -0.0008
## 240 3.9232 nan 0.0500 -0.0018
## 260 3.8710 nan 0.0500 -0.0108
## 280 3.8077 nan 0.0500 -0.0006
## 300 3.7601 nan 0.0500 -0.0061
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.3583 nan 0.0500 3.7702
## 2 54.6627 nan 0.0500 3.3922
## 3 51.5871 nan 0.0500 2.9825
## 4 48.6161 nan 0.0500 2.8540
## 5 45.7592 nan 0.0500 2.4412
## 6 43.2429 nan 0.0500 2.3682
## 7 40.8529 nan 0.0500 2.1225
## 8 38.8422 nan 0.0500 1.9111
## 9 36.8274 nan 0.0500 1.6896
## 10 35.1189 nan 0.0500 1.6276
## 20 22.6289 nan 0.0500 0.8623
## 40 12.0454 nan 0.0500 0.3335
## 60 8.0545 nan 0.0500 0.0532
## 80 6.0855 nan 0.0500 0.0602
## 100 5.1732 nan 0.0500 0.0125
## 120 4.7647 nan 0.0500 0.0066
## 140 4.5707 nan 0.0500 -0.0092
## 160 4.4135 nan 0.0500 -0.0063
## 180 4.2780 nan 0.0500 -0.0085
## 200 4.1921 nan 0.0500 -0.0186
## 220 4.1194 nan 0.0500 0.0002
## 240 4.0499 nan 0.0500 -0.0172
## 260 4.0078 nan 0.0500 -0.0100
## 280 3.9571 nan 0.0500 -0.0178
## 300 3.9042 nan 0.0500 -0.0222
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.1475 nan 0.0500 3.4288
## 2 54.9305 nan 0.0500 3.3203
## 3 51.6950 nan 0.0500 3.1132
## 4 48.5420 nan 0.0500 2.7732
## 5 45.8491 nan 0.0500 2.4686
## 6 43.7292 nan 0.0500 1.9523
## 7 41.5442 nan 0.0500 2.3096
## 8 39.2984 nan 0.0500 2.0395
## 9 37.3538 nan 0.0500 1.8446
## 10 35.5245 nan 0.0500 1.6948
## 20 22.5341 nan 0.0500 0.8060
## 40 11.8316 nan 0.0500 0.2360
## 60 7.7154 nan 0.0500 0.1049
## 80 6.0048 nan 0.0500 0.0531
## 100 5.2107 nan 0.0500 -0.0017
## 120 4.8325 nan 0.0500 -0.0099
## 140 4.6091 nan 0.0500 -0.0144
## 160 4.4715 nan 0.0500 -0.0115
## 180 4.3627 nan 0.0500 -0.0154
## 200 4.2785 nan 0.0500 -0.0046
## 220 4.1841 nan 0.0500 -0.0057
## 240 4.1275 nan 0.0500 -0.0297
## 260 4.0548 nan 0.0500 -0.0211
## 280 3.9984 nan 0.0500 -0.0098
## 300 3.9480 nan 0.0500 -0.0178
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.8468 nan 0.0500 4.9823
## 2 52.2897 nan 0.0500 4.2509
## 3 48.2493 nan 0.0500 3.8500
## 4 44.2044 nan 0.0500 3.9502
## 5 40.9998 nan 0.0500 3.1167
## 6 37.9338 nan 0.0500 3.5273
## 7 35.0813 nan 0.0500 2.7784
## 8 32.5656 nan 0.0500 2.6083
## 9 30.2512 nan 0.0500 2.3750
## 10 28.1495 nan 0.0500 1.8037
## 20 14.6402 nan 0.0500 0.7339
## 40 6.2141 nan 0.0500 0.2007
## 60 4.1856 nan 0.0500 0.0297
## 80 3.5389 nan 0.0500 -0.0063
## 100 3.2361 nan 0.0500 -0.0130
## 120 2.9660 nan 0.0500 -0.0140
## 140 2.7125 nan 0.0500 -0.0170
## 160 2.5298 nan 0.0500 -0.0096
## 180 2.3619 nan 0.0500 -0.0103
## 200 2.2383 nan 0.0500 -0.0148
## 220 2.1349 nan 0.0500 -0.0065
## 240 2.0271 nan 0.0500 -0.0095
## 260 1.9195 nan 0.0500 -0.0094
## 280 1.8284 nan 0.0500 -0.0074
## 300 1.7672 nan 0.0500 -0.0156
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.9699 nan 0.0500 4.8253
## 2 52.2343 nan 0.0500 4.3562
## 3 48.2032 nan 0.0500 3.6253
## 4 44.6032 nan 0.0500 3.7686
## 5 41.6168 nan 0.0500 3.4270
## 6 38.4945 nan 0.0500 3.2866
## 7 35.6761 nan 0.0500 2.6693
## 8 33.0318 nan 0.0500 2.6381
## 9 30.6494 nan 0.0500 2.4535
## 10 28.4127 nan 0.0500 2.2697
## 20 14.8107 nan 0.0500 0.8712
## 40 6.3471 nan 0.0500 0.1360
## 60 4.3693 nan 0.0500 0.0035
## 80 3.7617 nan 0.0500 0.0149
## 100 3.4241 nan 0.0500 -0.0335
## 120 3.2004 nan 0.0500 -0.0164
## 140 3.0228 nan 0.0500 -0.0222
## 160 2.8736 nan 0.0500 -0.0215
## 180 2.7380 nan 0.0500 -0.0256
## 200 2.6255 nan 0.0500 -0.0063
## 220 2.5174 nan 0.0500 -0.0053
## 240 2.4024 nan 0.0500 -0.0222
## 260 2.3129 nan 0.0500 -0.0090
## 280 2.2234 nan 0.0500 -0.0149
## 300 2.1569 nan 0.0500 -0.0081
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.0318 nan 0.0500 5.2241
## 2 52.3588 nan 0.0500 4.3338
## 3 48.2314 nan 0.0500 3.8491
## 4 44.6297 nan 0.0500 3.8542
## 5 41.2513 nan 0.0500 3.3895
## 6 37.9487 nan 0.0500 2.9241
## 7 35.1918 nan 0.0500 2.8625
## 8 32.4621 nan 0.0500 2.2926
## 9 30.1004 nan 0.0500 2.1692
## 10 28.0571 nan 0.0500 2.1696
## 20 14.7817 nan 0.0500 0.8538
## 40 6.6302 nan 0.0500 0.1822
## 60 4.6501 nan 0.0500 0.0040
## 80 4.0142 nan 0.0500 0.0014
## 100 3.7017 nan 0.0500 -0.0054
## 120 3.4814 nan 0.0500 -0.0426
## 140 3.2993 nan 0.0500 -0.0059
## 160 3.1524 nan 0.0500 -0.0381
## 180 3.0405 nan 0.0500 -0.0204
## 200 2.9298 nan 0.0500 -0.0072
## 220 2.8203 nan 0.0500 -0.0190
## 240 2.7347 nan 0.0500 -0.0033
## 260 2.6470 nan 0.0500 -0.0074
## 280 2.5829 nan 0.0500 -0.0202
## 300 2.4910 nan 0.0500 -0.0143
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.4199 nan 0.0500 6.1120
## 2 51.5760 nan 0.0500 4.7250
## 3 47.2701 nan 0.0500 4.0343
## 4 43.4141 nan 0.0500 3.8000
## 5 39.8707 nan 0.0500 3.5911
## 6 36.6415 nan 0.0500 3.0290
## 7 33.9256 nan 0.0500 2.8129
## 8 31.2809 nan 0.0500 2.3267
## 9 28.8015 nan 0.0500 2.0608
## 10 26.4720 nan 0.0500 1.8536
## 20 12.7148 nan 0.0500 0.8396
## 40 5.1527 nan 0.0500 0.1045
## 60 3.5056 nan 0.0500 0.0233
## 80 2.9058 nan 0.0500 0.0064
## 100 2.5195 nan 0.0500 -0.0095
## 120 2.2780 nan 0.0500 -0.0139
## 140 2.0464 nan 0.0500 -0.0225
## 160 1.8618 nan 0.0500 -0.0086
## 180 1.6955 nan 0.0500 -0.0071
## 200 1.5669 nan 0.0500 -0.0114
## 220 1.4465 nan 0.0500 -0.0047
## 240 1.3395 nan 0.0500 -0.0188
## 260 1.2408 nan 0.0500 -0.0101
## 280 1.1445 nan 0.0500 -0.0082
## 300 1.0658 nan 0.0500 -0.0069
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.7837 nan 0.0500 4.9749
## 2 51.9329 nan 0.0500 4.7119
## 3 47.7165 nan 0.0500 4.2591
## 4 43.7532 nan 0.0500 3.4393
## 5 40.3199 nan 0.0500 3.7191
## 6 37.1990 nan 0.0500 3.0032
## 7 34.1873 nan 0.0500 2.4679
## 8 31.5847 nan 0.0500 2.8507
## 9 29.1043 nan 0.0500 2.2560
## 10 26.8693 nan 0.0500 1.9285
## 20 13.4486 nan 0.0500 0.7077
## 40 5.4059 nan 0.0500 0.1273
## 60 3.7388 nan 0.0500 0.0116
## 80 3.1674 nan 0.0500 -0.0052
## 100 2.8128 nan 0.0500 -0.0083
## 120 2.5731 nan 0.0500 -0.0296
## 140 2.3753 nan 0.0500 -0.0186
## 160 2.2237 nan 0.0500 -0.0123
## 180 2.0932 nan 0.0500 -0.0134
## 200 1.9533 nan 0.0500 -0.0101
## 220 1.8481 nan 0.0500 -0.0152
## 240 1.7331 nan 0.0500 -0.0098
## 260 1.6449 nan 0.0500 -0.0103
## 280 1.5608 nan 0.0500 -0.0136
## 300 1.4904 nan 0.0500 -0.0185
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.8951 nan 0.0500 4.6450
## 2 52.0892 nan 0.0500 4.7157
## 3 47.7050 nan 0.0500 4.1144
## 4 43.7895 nan 0.0500 3.8474
## 5 40.3450 nan 0.0500 3.4873
## 6 37.1714 nan 0.0500 2.8507
## 7 34.1761 nan 0.0500 2.7566
## 8 31.4909 nan 0.0500 2.6038
## 9 29.0280 nan 0.0500 2.3011
## 10 26.8480 nan 0.0500 1.7609
## 20 12.9348 nan 0.0500 0.8350
## 40 5.5096 nan 0.0500 0.1303
## 60 4.0999 nan 0.0500 -0.0008
## 80 3.6296 nan 0.0500 -0.0071
## 100 3.3475 nan 0.0500 -0.0137
## 120 3.1169 nan 0.0500 -0.0224
## 140 2.9306 nan 0.0500 -0.0156
## 160 2.7564 nan 0.0500 -0.0088
## 180 2.6169 nan 0.0500 -0.0187
## 200 2.4813 nan 0.0500 -0.0045
## 220 2.3481 nan 0.0500 -0.0080
## 240 2.2431 nan 0.0500 -0.0033
## 260 2.1423 nan 0.0500 -0.0151
## 280 2.0568 nan 0.0500 -0.0260
## 300 1.9780 nan 0.0500 -0.0131
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.4942 nan 0.1000 7.4367
## 2 48.1030 nan 0.1000 6.0468
## 3 43.1471 nan 0.1000 4.8749
## 4 39.1182 nan 0.1000 4.1506
## 5 35.2624 nan 0.1000 3.8812
## 6 32.0207 nan 0.1000 3.1352
## 7 29.1942 nan 0.1000 2.7367
## 8 26.4993 nan 0.1000 2.3297
## 9 24.4903 nan 0.1000 1.9794
## 10 22.6163 nan 0.1000 1.4241
## 20 11.6790 nan 0.1000 0.4447
## 40 5.9052 nan 0.1000 0.1023
## 60 4.7377 nan 0.1000 0.0111
## 80 4.3624 nan 0.1000 -0.0610
## 100 4.1634 nan 0.1000 0.0030
## 120 3.9861 nan 0.1000 -0.0143
## 140 3.8581 nan 0.1000 0.0050
## 160 3.7432 nan 0.1000 -0.0134
## 180 3.6361 nan 0.1000 0.0018
## 200 3.5595 nan 0.1000 -0.0672
## 220 3.4629 nan 0.1000 -0.0114
## 240 3.3850 nan 0.1000 -0.0203
## 260 3.3223 nan 0.1000 -0.0070
## 280 3.2471 nan 0.1000 -0.0141
## 300 3.1868 nan 0.1000 -0.0422
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.3703 nan 0.1000 7.5508
## 2 48.1661 nan 0.1000 5.7058
## 3 42.9969 nan 0.1000 5.6435
## 4 38.7920 nan 0.1000 3.8153
## 5 35.0737 nan 0.1000 3.4045
## 6 31.5012 nan 0.1000 3.1884
## 7 28.3632 nan 0.1000 2.8261
## 8 25.9607 nan 0.1000 2.2638
## 9 23.9589 nan 0.1000 2.2215
## 10 22.1517 nan 0.1000 1.7520
## 20 11.4952 nan 0.1000 0.5707
## 40 6.0919 nan 0.1000 0.0186
## 60 4.8302 nan 0.1000 0.0059
## 80 4.5014 nan 0.1000 -0.0423
## 100 4.2902 nan 0.1000 -0.0120
## 120 4.1085 nan 0.1000 -0.0136
## 140 3.9673 nan 0.1000 -0.0211
## 160 3.8808 nan 0.1000 -0.0444
## 180 3.7951 nan 0.1000 -0.0263
## 200 3.6965 nan 0.1000 -0.0063
## 220 3.6136 nan 0.1000 -0.0085
## 240 3.5486 nan 0.1000 -0.0316
## 260 3.4780 nan 0.1000 -0.0060
## 280 3.4113 nan 0.1000 -0.0132
## 300 3.3488 nan 0.1000 -0.0232
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.5642 nan 0.1000 7.3150
## 2 48.3378 nan 0.1000 6.4479
## 3 42.7220 nan 0.1000 5.3661
## 4 38.3344 nan 0.1000 4.1302
## 5 34.7347 nan 0.1000 3.7821
## 6 31.4075 nan 0.1000 3.1892
## 7 28.9008 nan 0.1000 2.4299
## 8 26.3632 nan 0.1000 1.9049
## 9 24.3865 nan 0.1000 2.0272
## 10 22.5833 nan 0.1000 1.7961
## 20 11.9961 nan 0.1000 0.5546
## 40 6.0683 nan 0.1000 0.0527
## 60 4.8348 nan 0.1000 -0.0322
## 80 4.5493 nan 0.1000 -0.0050
## 100 4.3309 nan 0.1000 -0.0095
## 120 4.1797 nan 0.1000 -0.0638
## 140 4.0479 nan 0.1000 -0.0234
## 160 3.9653 nan 0.1000 -0.0235
## 180 3.8935 nan 0.1000 -0.0112
## 200 3.8346 nan 0.1000 -0.0406
## 220 3.7815 nan 0.1000 -0.0161
## 240 3.7158 nan 0.1000 -0.0452
## 260 3.6649 nan 0.1000 -0.0427
## 280 3.6115 nan 0.1000 -0.0052
## 300 3.5601 nan 0.1000 -0.0305
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.0842 nan 0.1000 9.3246
## 2 44.1789 nan 0.1000 7.8135
## 3 38.0786 nan 0.1000 5.3407
## 4 32.9572 nan 0.1000 5.6217
## 5 28.3216 nan 0.1000 4.1822
## 6 24.5225 nan 0.1000 3.5355
## 7 21.6613 nan 0.1000 3.0743
## 8 18.9192 nan 0.1000 2.6779
## 9 16.9583 nan 0.1000 2.0803
## 10 15.0642 nan 0.1000 1.8737
## 20 6.4147 nan 0.1000 0.3294
## 40 3.7682 nan 0.1000 -0.0453
## 60 3.1294 nan 0.1000 -0.0218
## 80 2.6326 nan 0.1000 -0.0061
## 100 2.3285 nan 0.1000 -0.0259
## 120 2.0895 nan 0.1000 -0.0145
## 140 1.8711 nan 0.1000 -0.0135
## 160 1.6898 nan 0.1000 -0.0104
## 180 1.5611 nan 0.1000 -0.0145
## 200 1.4394 nan 0.1000 -0.0098
## 220 1.3228 nan 0.1000 -0.0275
## 240 1.2025 nan 0.1000 -0.0196
## 260 1.1222 nan 0.1000 -0.0112
## 280 1.0373 nan 0.1000 -0.0068
## 300 0.9568 nan 0.1000 -0.0068
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.2851 nan 0.1000 9.4219
## 2 43.8049 nan 0.1000 6.9725
## 3 37.1925 nan 0.1000 6.5238
## 4 32.1329 nan 0.1000 4.1976
## 5 27.7888 nan 0.1000 3.9423
## 6 24.1978 nan 0.1000 2.5259
## 7 21.2532 nan 0.1000 2.8739
## 8 18.6324 nan 0.1000 2.4699
## 9 16.3798 nan 0.1000 1.4447
## 10 14.3329 nan 0.1000 1.8704
## 20 6.4610 nan 0.1000 0.2738
## 40 3.7799 nan 0.1000 -0.0184
## 60 3.1996 nan 0.1000 -0.0145
## 80 2.8827 nan 0.1000 -0.0418
## 100 2.6678 nan 0.1000 -0.0124
## 120 2.4172 nan 0.1000 -0.0390
## 140 2.2186 nan 0.1000 -0.0341
## 160 2.0577 nan 0.1000 -0.0232
## 180 1.9114 nan 0.1000 -0.0251
## 200 1.7889 nan 0.1000 -0.0183
## 220 1.6888 nan 0.1000 -0.0361
## 240 1.5788 nan 0.1000 -0.0148
## 260 1.5009 nan 0.1000 -0.0247
## 280 1.4380 nan 0.1000 -0.0233
## 300 1.3397 nan 0.1000 -0.0095
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.2246 nan 0.1000 10.4952
## 2 44.9604 nan 0.1000 7.0779
## 3 38.0291 nan 0.1000 6.3631
## 4 33.0004 nan 0.1000 5.4854
## 5 28.2929 nan 0.1000 3.8314
## 6 24.5448 nan 0.1000 3.5218
## 7 21.5809 nan 0.1000 3.0567
## 8 19.0817 nan 0.1000 2.0424
## 9 16.8944 nan 0.1000 2.1184
## 10 15.1068 nan 0.1000 1.7025
## 20 6.5239 nan 0.1000 0.2687
## 40 4.0757 nan 0.1000 -0.0082
## 60 3.6032 nan 0.1000 -0.0299
## 80 3.2191 nan 0.1000 -0.0143
## 100 2.9313 nan 0.1000 -0.0232
## 120 2.7037 nan 0.1000 -0.0126
## 140 2.5192 nan 0.1000 -0.0098
## 160 2.3595 nan 0.1000 -0.0099
## 180 2.2522 nan 0.1000 -0.0288
## 200 2.1502 nan 0.1000 -0.0191
## 220 2.0633 nan 0.1000 -0.0290
## 240 1.9872 nan 0.1000 -0.0178
## 260 1.8956 nan 0.1000 -0.0396
## 280 1.8139 nan 0.1000 -0.0134
## 300 1.7371 nan 0.1000 -0.0157
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.4508 nan 0.1000 9.6105
## 2 42.7810 nan 0.1000 7.7365
## 3 35.9116 nan 0.1000 7.0000
## 4 30.1271 nan 0.1000 5.4712
## 5 25.6356 nan 0.1000 4.3297
## 6 21.7710 nan 0.1000 3.0801
## 7 18.9673 nan 0.1000 3.0326
## 8 16.1625 nan 0.1000 2.6169
## 9 14.1362 nan 0.1000 1.8250
## 10 12.5215 nan 0.1000 1.3520
## 20 5.0271 nan 0.1000 0.2604
## 40 2.8100 nan 0.1000 -0.0053
## 60 2.1302 nan 0.1000 -0.0253
## 80 1.8126 nan 0.1000 -0.0342
## 100 1.5325 nan 0.1000 -0.0089
## 120 1.3051 nan 0.1000 -0.0288
## 140 1.0968 nan 0.1000 -0.0055
## 160 0.9408 nan 0.1000 -0.0174
## 180 0.8326 nan 0.1000 -0.0207
## 200 0.7318 nan 0.1000 -0.0101
## 220 0.6465 nan 0.1000 -0.0093
## 240 0.5778 nan 0.1000 -0.0209
## 260 0.5145 nan 0.1000 -0.0096
## 280 0.4642 nan 0.1000 -0.0078
## 300 0.4211 nan 0.1000 -0.0088
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.8478 nan 0.1000 9.4693
## 2 43.7489 nan 0.1000 8.0990
## 3 36.4801 nan 0.1000 5.8398
## 4 30.9473 nan 0.1000 5.3816
## 5 26.2613 nan 0.1000 4.2821
## 6 22.4795 nan 0.1000 3.1843
## 7 19.3738 nan 0.1000 2.8526
## 8 16.5866 nan 0.1000 2.2636
## 9 14.5540 nan 0.1000 1.8696
## 10 12.7029 nan 0.1000 1.6632
## 20 5.2177 nan 0.1000 0.1820
## 40 3.1737 nan 0.1000 -0.0396
## 60 2.6599 nan 0.1000 0.0013
## 80 2.3341 nan 0.1000 -0.0461
## 100 2.0495 nan 0.1000 -0.0389
## 120 1.8359 nan 0.1000 -0.0479
## 140 1.6264 nan 0.1000 -0.0224
## 160 1.4851 nan 0.1000 -0.0165
## 180 1.3316 nan 0.1000 -0.0085
## 200 1.2032 nan 0.1000 -0.0142
## 220 1.1055 nan 0.1000 -0.0185
## 240 1.0264 nan 0.1000 -0.0125
## 260 0.9342 nan 0.1000 -0.0153
## 280 0.8672 nan 0.1000 -0.0285
## 300 0.8035 nan 0.1000 -0.0138
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.9098 nan 0.1000 11.5385
## 2 42.7999 nan 0.1000 8.4313
## 3 36.1975 nan 0.1000 6.2216
## 4 30.6846 nan 0.1000 5.3458
## 5 26.2712 nan 0.1000 4.3463
## 6 22.6375 nan 0.1000 3.3744
## 7 19.4777 nan 0.1000 2.9858
## 8 16.8529 nan 0.1000 2.6947
## 9 14.7269 nan 0.1000 2.2670
## 10 12.8792 nan 0.1000 1.8642
## 20 5.5631 nan 0.1000 0.2767
## 40 3.6375 nan 0.1000 -0.0077
## 60 3.0863 nan 0.1000 -0.0401
## 80 2.7156 nan 0.1000 -0.0403
## 100 2.4505 nan 0.1000 -0.0594
## 120 2.2095 nan 0.1000 -0.0396
## 140 2.0110 nan 0.1000 -0.0229
## 160 1.8691 nan 0.1000 -0.0299
## 180 1.7029 nan 0.1000 -0.0270
## 200 1.5728 nan 0.1000 -0.0415
## 220 1.4702 nan 0.1000 -0.0198
## 240 1.3920 nan 0.1000 -0.0149
## 260 1.2846 nan 0.1000 -0.0267
## 280 1.2200 nan 0.1000 -0.0155
## 300 1.1637 nan 0.1000 -0.0109
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.6823 nan 0.0100 0.7368
## 2 58.9940 nan 0.0100 0.7574
## 3 58.2190 nan 0.0100 0.7148
## 4 57.5109 nan 0.0100 0.7034
## 5 56.7842 nan 0.0100 0.7185
## 6 56.0852 nan 0.0100 0.6905
## 7 55.4319 nan 0.0100 0.6603
## 8 54.7807 nan 0.0100 0.6777
## 9 54.0980 nan 0.0100 0.6482
## 10 53.4720 nan 0.0100 0.6282
## 20 47.4933 nan 0.0100 0.5268
## 40 38.3015 nan 0.0100 0.3697
## 60 31.4362 nan 0.0100 0.2878
## 80 26.1281 nan 0.0100 0.2077
## 100 22.0564 nan 0.0100 0.1724
## 120 18.9299 nan 0.0100 0.1284
## 140 16.4093 nan 0.0100 0.0435
## 160 14.3666 nan 0.0100 0.0787
## 180 12.7596 nan 0.0100 0.0684
## 200 11.4469 nan 0.0100 0.0551
## 220 10.3126 nan 0.0100 0.0414
## 240 9.4292 nan 0.0100 0.0337
## 260 8.6507 nan 0.0100 0.0331
## 280 8.0116 nan 0.0100 0.0139
## 300 7.4394 nan 0.0100 0.0091
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.6100 nan 0.0100 0.7405
## 2 58.7868 nan 0.0100 0.7641
## 3 57.9979 nan 0.0100 0.7232
## 4 57.2784 nan 0.0100 0.6819
## 5 56.6536 nan 0.0100 0.6686
## 6 55.9197 nan 0.0100 0.6963
## 7 55.1589 nan 0.0100 0.6507
## 8 54.4953 nan 0.0100 0.6717
## 9 53.8667 nan 0.0100 0.6275
## 10 53.2052 nan 0.0100 0.5868
## 20 47.3637 nan 0.0100 0.5249
## 40 38.3072 nan 0.0100 0.3386
## 60 31.4029 nan 0.0100 0.2052
## 80 26.1564 nan 0.0100 0.2181
## 100 21.9544 nan 0.0100 0.1785
## 120 18.8067 nan 0.0100 0.1058
## 140 16.3365 nan 0.0100 0.0915
## 160 14.3781 nan 0.0100 0.0808
## 180 12.7709 nan 0.0100 0.0517
## 200 11.4239 nan 0.0100 0.0570
## 220 10.3280 nan 0.0100 0.0619
## 240 9.4318 nan 0.0100 0.0239
## 260 8.6486 nan 0.0100 0.0196
## 280 7.9889 nan 0.0100 0.0245
## 300 7.4131 nan 0.0100 0.0191
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.6694 nan 0.0100 0.7677
## 2 58.9584 nan 0.0100 0.7666
## 3 58.1586 nan 0.0100 0.7000
## 4 57.5024 nan 0.0100 0.6179
## 5 56.8155 nan 0.0100 0.7314
## 6 56.1038 nan 0.0100 0.6908
## 7 55.3817 nan 0.0100 0.6575
## 8 54.7487 nan 0.0100 0.6695
## 9 54.0907 nan 0.0100 0.6266
## 10 53.4235 nan 0.0100 0.6263
## 20 47.6873 nan 0.0100 0.5010
## 40 38.4080 nan 0.0100 0.3874
## 60 31.3738 nan 0.0100 0.3018
## 80 26.1046 nan 0.0100 0.2256
## 100 22.0431 nan 0.0100 0.1820
## 120 18.8617 nan 0.0100 0.1221
## 140 16.3776 nan 0.0100 0.1023
## 160 14.4163 nan 0.0100 0.0582
## 180 12.8240 nan 0.0100 0.0686
## 200 11.5007 nan 0.0100 0.0537
## 220 10.3860 nan 0.0100 0.0481
## 240 9.5198 nan 0.0100 0.0288
## 260 8.7751 nan 0.0100 0.0267
## 280 8.1175 nan 0.0100 0.0248
## 300 7.5633 nan 0.0100 0.0173
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.4537 nan 0.0100 0.8790
## 2 58.5173 nan 0.0100 0.8837
## 3 57.5514 nan 0.0100 0.8984
## 4 56.6026 nan 0.0100 0.8982
## 5 55.6625 nan 0.0100 0.8371
## 6 54.7812 nan 0.0100 0.8885
## 7 53.8758 nan 0.0100 0.8326
## 8 53.0011 nan 0.0100 0.9246
## 9 52.1731 nan 0.0100 0.8596
## 10 51.3352 nan 0.0100 0.7578
## 20 43.6794 nan 0.0100 0.6195
## 40 32.2301 nan 0.0100 0.4822
## 60 24.2577 nan 0.0100 0.3160
## 80 18.6253 nan 0.0100 0.2283
## 100 14.6195 nan 0.0100 0.1435
## 120 11.7307 nan 0.0100 0.1277
## 140 9.5705 nan 0.0100 0.0827
## 160 8.0502 nan 0.0100 0.0475
## 180 6.9022 nan 0.0100 0.0449
## 200 6.0159 nan 0.0100 0.0300
## 220 5.3758 nan 0.0100 0.0240
## 240 4.8923 nan 0.0100 0.0122
## 260 4.5289 nan 0.0100 0.0105
## 280 4.2590 nan 0.0100 0.0054
## 300 4.0390 nan 0.0100 0.0027
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.4854 nan 0.0100 1.0315
## 2 58.5040 nan 0.0100 0.9146
## 3 57.5697 nan 0.0100 0.8463
## 4 56.6588 nan 0.0100 0.9269
## 5 55.7566 nan 0.0100 0.7372
## 6 54.8282 nan 0.0100 0.9267
## 7 53.9807 nan 0.0100 0.9041
## 8 53.0988 nan 0.0100 0.8590
## 9 52.2335 nan 0.0100 0.7957
## 10 51.4233 nan 0.0100 0.7834
## 20 43.8350 nan 0.0100 0.6357
## 40 32.2412 nan 0.0100 0.4445
## 60 24.1629 nan 0.0100 0.2989
## 80 18.6372 nan 0.0100 0.2468
## 100 14.6186 nan 0.0100 0.1670
## 120 11.7218 nan 0.0100 0.1286
## 140 9.6425 nan 0.0100 0.0816
## 160 8.1241 nan 0.0100 0.0562
## 180 6.9762 nan 0.0100 0.0245
## 200 6.1034 nan 0.0100 0.0351
## 220 5.4673 nan 0.0100 0.0159
## 240 4.9795 nan 0.0100 0.0145
## 260 4.5980 nan 0.0100 0.0083
## 280 4.3341 nan 0.0100 0.0057
## 300 4.1309 nan 0.0100 0.0039
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.3985 nan 0.0100 0.9423
## 2 58.4228 nan 0.0100 1.0260
## 3 57.4807 nan 0.0100 0.8263
## 4 56.5449 nan 0.0100 1.0227
## 5 55.6241 nan 0.0100 0.8614
## 6 54.7140 nan 0.0100 0.9334
## 7 53.8096 nan 0.0100 0.8481
## 8 53.0135 nan 0.0100 0.8680
## 9 52.1557 nan 0.0100 0.8153
## 10 51.3196 nan 0.0100 0.8642
## 20 43.6644 nan 0.0100 0.6664
## 40 32.1502 nan 0.0100 0.4758
## 60 24.3324 nan 0.0100 0.2923
## 80 18.6202 nan 0.0100 0.2149
## 100 14.6901 nan 0.0100 0.1642
## 120 11.9079 nan 0.0100 0.0740
## 140 9.9109 nan 0.0100 0.0675
## 160 8.3872 nan 0.0100 0.0491
## 180 7.2240 nan 0.0100 0.0454
## 200 6.4056 nan 0.0100 0.0321
## 220 5.7914 nan 0.0100 0.0203
## 240 5.3270 nan 0.0100 0.0153
## 260 4.9732 nan 0.0100 0.0131
## 280 4.6992 nan 0.0100 0.0058
## 300 4.4856 nan 0.0100 0.0045
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.3399 nan 0.0100 1.0410
## 2 58.3287 nan 0.0100 0.9834
## 3 57.2252 nan 0.0100 1.1228
## 4 56.2826 nan 0.0100 0.8837
## 5 55.2901 nan 0.0100 0.9723
## 6 54.3333 nan 0.0100 0.9093
## 7 53.3809 nan 0.0100 0.8958
## 8 52.4560 nan 0.0100 0.9086
## 9 51.6023 nan 0.0100 0.8679
## 10 50.7378 nan 0.0100 0.8417
## 20 42.8202 nan 0.0100 0.7165
## 40 30.9214 nan 0.0100 0.4491
## 60 22.7483 nan 0.0100 0.3333
## 80 16.9485 nan 0.0100 0.2021
## 100 12.8695 nan 0.0100 0.1425
## 120 10.0022 nan 0.0100 0.0966
## 140 8.0450 nan 0.0100 0.0707
## 160 6.6209 nan 0.0100 0.0568
## 180 5.5803 nan 0.0100 0.0299
## 200 4.8577 nan 0.0100 0.0196
## 220 4.3469 nan 0.0100 0.0168
## 240 3.9497 nan 0.0100 0.0069
## 260 3.6457 nan 0.0100 0.0050
## 280 3.4158 nan 0.0100 0.0049
## 300 3.2262 nan 0.0100 -0.0022
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.3679 nan 0.0100 1.0587
## 2 58.3027 nan 0.0100 1.0086
## 3 57.2678 nan 0.0100 1.0492
## 4 56.2529 nan 0.0100 0.9949
## 5 55.3397 nan 0.0100 1.0062
## 6 54.3528 nan 0.0100 0.8128
## 7 53.4004 nan 0.0100 0.9332
## 8 52.4631 nan 0.0100 0.9586
## 9 51.5575 nan 0.0100 0.9106
## 10 50.6794 nan 0.0100 0.9015
## 20 42.6847 nan 0.0100 0.6385
## 40 30.6552 nan 0.0100 0.5083
## 60 22.4710 nan 0.0100 0.3535
## 80 16.8240 nan 0.0100 0.2058
## 100 12.8683 nan 0.0100 0.1474
## 120 10.0577 nan 0.0100 0.1039
## 140 8.1552 nan 0.0100 0.0756
## 160 6.7648 nan 0.0100 0.0619
## 180 5.7506 nan 0.0100 0.0338
## 200 5.0544 nan 0.0100 0.0254
## 220 4.5420 nan 0.0100 0.0140
## 240 4.1540 nan 0.0100 0.0048
## 260 3.8496 nan 0.0100 0.0045
## 280 3.6260 nan 0.0100 0.0008
## 300 3.4483 nan 0.0100 0.0017
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.3479 nan 0.0100 1.0742
## 2 58.3655 nan 0.0100 1.0188
## 3 57.3481 nan 0.0100 0.9436
## 4 56.3870 nan 0.0100 0.9702
## 5 55.4357 nan 0.0100 0.8858
## 6 54.5114 nan 0.0100 0.8888
## 7 53.5687 nan 0.0100 0.8814
## 8 52.6887 nan 0.0100 0.8102
## 9 51.7623 nan 0.0100 0.9391
## 10 50.8459 nan 0.0100 0.7667
## 20 43.0407 nan 0.0100 0.7046
## 40 31.0539 nan 0.0100 0.5283
## 60 22.9464 nan 0.0100 0.3422
## 80 17.2216 nan 0.0100 0.2431
## 100 13.2803 nan 0.0100 0.1444
## 120 10.4537 nan 0.0100 0.1020
## 140 8.5716 nan 0.0100 0.0816
## 160 7.1649 nan 0.0100 0.0433
## 180 6.1610 nan 0.0100 0.0347
## 200 5.4470 nan 0.0100 0.0252
## 220 4.9239 nan 0.0100 0.0133
## 240 4.5613 nan 0.0100 0.0031
## 260 4.2796 nan 0.0100 0.0029
## 280 4.0610 nan 0.0100 0.0046
## 300 3.9013 nan 0.0100 0.0034
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.6261 nan 0.0500 3.7487
## 2 53.7912 nan 0.0500 2.7395
## 3 50.7649 nan 0.0500 3.2602
## 4 47.8375 nan 0.0500 3.0468
## 5 45.3476 nan 0.0500 2.6203
## 6 42.9063 nan 0.0500 2.3380
## 7 40.5140 nan 0.0500 2.2158
## 8 38.3436 nan 0.0500 2.0480
## 9 36.3411 nan 0.0500 2.3154
## 10 34.4406 nan 0.0500 1.6555
## 20 21.5898 nan 0.0500 0.8591
## 40 11.0134 nan 0.0500 0.2203
## 60 7.3240 nan 0.0500 0.0550
## 80 5.6272 nan 0.0500 0.0217
## 100 4.8134 nan 0.0500 0.0298
## 120 4.4378 nan 0.0500 -0.0111
## 140 4.2641 nan 0.0500 -0.0120
## 160 4.1355 nan 0.0500 -0.0072
## 180 4.0309 nan 0.0500 -0.0101
## 200 3.9671 nan 0.0500 -0.0051
## 220 3.8848 nan 0.0500 -0.0197
## 240 3.8219 nan 0.0500 -0.0204
## 260 3.7512 nan 0.0500 -0.0056
## 280 3.6941 nan 0.0500 -0.0100
## 300 3.6495 nan 0.0500 -0.0182
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.8668 nan 0.0500 3.5521
## 2 53.5744 nan 0.0500 3.2931
## 3 50.4557 nan 0.0500 2.8791
## 4 47.4603 nan 0.0500 2.8969
## 5 44.8641 nan 0.0500 2.4955
## 6 42.5790 nan 0.0500 2.3619
## 7 40.2903 nan 0.0500 2.1625
## 8 38.0764 nan 0.0500 1.9872
## 9 36.1914 nan 0.0500 1.7092
## 10 34.4225 nan 0.0500 1.4870
## 20 21.6748 nan 0.0500 0.8449
## 40 11.3266 nan 0.0500 0.2378
## 60 7.4220 nan 0.0500 0.0895
## 80 5.6884 nan 0.0500 0.0698
## 100 4.8437 nan 0.0500 -0.0277
## 120 4.4746 nan 0.0500 -0.0180
## 140 4.3092 nan 0.0500 -0.0166
## 160 4.1675 nan 0.0500 0.0011
## 180 4.0536 nan 0.0500 -0.0089
## 200 3.9674 nan 0.0500 0.0001
## 220 3.9136 nan 0.0500 -0.0048
## 240 3.8534 nan 0.0500 -0.0077
## 260 3.8007 nan 0.0500 -0.0060
## 280 3.7443 nan 0.0500 -0.0072
## 300 3.6985 nan 0.0500 -0.0055
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.5783 nan 0.0500 3.6804
## 2 53.1842 nan 0.0500 3.1426
## 3 50.0170 nan 0.0500 3.0461
## 4 47.1402 nan 0.0500 2.6536
## 5 44.5611 nan 0.0500 2.5262
## 6 42.0138 nan 0.0500 2.5241
## 7 39.7197 nan 0.0500 1.9784
## 8 37.7440 nan 0.0500 2.0082
## 9 35.8590 nan 0.0500 1.7508
## 10 34.0054 nan 0.0500 1.5630
## 20 21.5637 nan 0.0500 0.7070
## 40 11.2986 nan 0.0500 0.3113
## 60 7.4859 nan 0.0500 0.1079
## 80 5.8868 nan 0.0500 0.0385
## 100 5.1682 nan 0.0500 0.0113
## 120 4.8127 nan 0.0500 -0.0073
## 140 4.6199 nan 0.0500 0.0005
## 160 4.4894 nan 0.0500 -0.0013
## 180 4.3780 nan 0.0500 -0.0085
## 200 4.2904 nan 0.0500 -0.0062
## 220 4.2179 nan 0.0500 -0.0164
## 240 4.1445 nan 0.0500 -0.0062
## 260 4.0778 nan 0.0500 -0.0051
## 280 4.0302 nan 0.0500 -0.0204
## 300 3.9868 nan 0.0500 -0.0134
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.9390 nan 0.0500 4.5195
## 2 51.3501 nan 0.0500 4.3905
## 3 47.3501 nan 0.0500 3.3966
## 4 43.4784 nan 0.0500 3.4441
## 5 40.0792 nan 0.0500 3.3657
## 6 37.1872 nan 0.0500 2.7718
## 7 34.4842 nan 0.0500 2.8705
## 8 31.8459 nan 0.0500 2.2082
## 9 29.4891 nan 0.0500 2.2506
## 10 27.3931 nan 0.0500 2.0703
## 20 14.2410 nan 0.0500 0.7321
## 40 6.0520 nan 0.0500 0.1605
## 60 4.1846 nan 0.0500 0.0145
## 80 3.4837 nan 0.0500 0.0023
## 100 3.1241 nan 0.0500 -0.0041
## 120 2.8952 nan 0.0500 -0.0096
## 140 2.7131 nan 0.0500 -0.0124
## 160 2.5656 nan 0.0500 -0.0012
## 180 2.4045 nan 0.0500 -0.0087
## 200 2.2753 nan 0.0500 -0.0159
## 220 2.1630 nan 0.0500 -0.0185
## 240 2.0601 nan 0.0500 -0.0090
## 260 1.9821 nan 0.0500 -0.0217
## 280 1.8794 nan 0.0500 -0.0135
## 300 1.8027 nan 0.0500 -0.0101
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.7343 nan 0.0500 4.8378
## 2 51.0956 nan 0.0500 3.9425
## 3 47.1105 nan 0.0500 4.1542
## 4 43.6623 nan 0.0500 3.4586
## 5 40.3779 nan 0.0500 3.2234
## 6 37.4336 nan 0.0500 2.8433
## 7 34.6588 nan 0.0500 3.1844
## 8 32.0552 nan 0.0500 2.2884
## 9 29.5180 nan 0.0500 2.4060
## 10 27.3576 nan 0.0500 1.9554
## 20 14.0868 nan 0.0500 0.7854
## 40 6.0087 nan 0.0500 0.1386
## 60 4.0308 nan 0.0500 0.0192
## 80 3.4844 nan 0.0500 -0.0071
## 100 3.1757 nan 0.0500 0.0057
## 120 2.9451 nan 0.0500 -0.0117
## 140 2.7599 nan 0.0500 -0.0066
## 160 2.6131 nan 0.0500 -0.0043
## 180 2.4732 nan 0.0500 -0.0046
## 200 2.3528 nan 0.0500 -0.0104
## 220 2.2511 nan 0.0500 -0.0119
## 240 2.1719 nan 0.0500 -0.0064
## 260 2.0978 nan 0.0500 -0.0170
## 280 2.0266 nan 0.0500 -0.0138
## 300 1.9598 nan 0.0500 -0.0075
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.7425 nan 0.0500 4.6921
## 2 51.3009 nan 0.0500 4.1635
## 3 47.3791 nan 0.0500 4.1524
## 4 43.8733 nan 0.0500 3.7272
## 5 40.5440 nan 0.0500 3.4299
## 6 37.4942 nan 0.0500 2.9157
## 7 34.7647 nan 0.0500 2.5271
## 8 32.2965 nan 0.0500 2.3437
## 9 29.8578 nan 0.0500 2.4670
## 10 27.6123 nan 0.0500 1.8093
## 20 14.5650 nan 0.0500 0.7572
## 40 6.3284 nan 0.0500 0.0840
## 60 4.4073 nan 0.0500 0.0195
## 80 3.8088 nan 0.0500 0.0057
## 100 3.5520 nan 0.0500 -0.0171
## 120 3.3050 nan 0.0500 -0.0224
## 140 3.1280 nan 0.0500 -0.0052
## 160 2.9630 nan 0.0500 -0.0273
## 180 2.8684 nan 0.0500 -0.0183
## 200 2.7673 nan 0.0500 -0.0063
## 220 2.6688 nan 0.0500 -0.0161
## 240 2.5867 nan 0.0500 -0.0197
## 260 2.5087 nan 0.0500 -0.0186
## 280 2.4237 nan 0.0500 -0.0203
## 300 2.3569 nan 0.0500 -0.0124
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.0482 nan 0.0500 5.3768
## 2 50.5553 nan 0.0500 4.6181
## 3 46.3952 nan 0.0500 4.0937
## 4 42.3138 nan 0.0500 3.9664
## 5 38.9461 nan 0.0500 3.0012
## 6 35.8536 nan 0.0500 2.9039
## 7 33.1376 nan 0.0500 2.6641
## 8 30.4637 nan 0.0500 2.3516
## 9 28.0884 nan 0.0500 2.2834
## 10 25.8278 nan 0.0500 2.2553
## 20 12.5306 nan 0.0500 0.8718
## 40 4.6861 nan 0.0500 0.1295
## 60 3.1509 nan 0.0500 -0.0100
## 80 2.6414 nan 0.0500 -0.0136
## 100 2.3102 nan 0.0500 -0.0137
## 120 2.0831 nan 0.0500 -0.0093
## 140 1.8696 nan 0.0500 -0.0117
## 160 1.6804 nan 0.0500 -0.0140
## 180 1.5325 nan 0.0500 -0.0080
## 200 1.4241 nan 0.0500 -0.0184
## 220 1.3063 nan 0.0500 -0.0132
## 240 1.2183 nan 0.0500 -0.0078
## 260 1.1336 nan 0.0500 -0.0098
## 280 1.0626 nan 0.0500 -0.0084
## 300 0.9816 nan 0.0500 -0.0084
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.1561 nan 0.0500 5.0999
## 2 50.5189 nan 0.0500 4.2868
## 3 46.2841 nan 0.0500 4.5820
## 4 42.6140 nan 0.0500 3.6570
## 5 39.0915 nan 0.0500 3.3062
## 6 36.0379 nan 0.0500 3.1714
## 7 32.8576 nan 0.0500 2.9433
## 8 30.2185 nan 0.0500 2.6580
## 9 27.8724 nan 0.0500 2.4005
## 10 25.7293 nan 0.0500 1.9413
## 20 12.5623 nan 0.0500 0.7227
## 40 4.9418 nan 0.0500 0.1395
## 60 3.4713 nan 0.0500 -0.0119
## 80 2.9431 nan 0.0500 -0.0307
## 100 2.6732 nan 0.0500 -0.0293
## 120 2.4515 nan 0.0500 -0.0147
## 140 2.2972 nan 0.0500 -0.0204
## 160 2.1346 nan 0.0500 -0.0275
## 180 1.9714 nan 0.0500 -0.0118
## 200 1.8353 nan 0.0500 -0.0234
## 220 1.7369 nan 0.0500 -0.0092
## 240 1.6407 nan 0.0500 -0.0108
## 260 1.5505 nan 0.0500 -0.0085
## 280 1.4689 nan 0.0500 -0.0135
## 300 1.3887 nan 0.0500 -0.0085
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.0419 nan 0.0500 5.1156
## 2 50.2492 nan 0.0500 4.5451
## 3 46.2944 nan 0.0500 3.9689
## 4 42.3943 nan 0.0500 4.0668
## 5 38.9155 nan 0.0500 3.1312
## 6 35.8146 nan 0.0500 2.7849
## 7 32.8144 nan 0.0500 2.6969
## 8 30.3687 nan 0.0500 2.4560
## 9 28.0512 nan 0.0500 2.3712
## 10 25.9431 nan 0.0500 2.1032
## 20 12.7705 nan 0.0500 0.7598
## 40 5.2424 nan 0.0500 0.1559
## 60 3.8161 nan 0.0500 -0.0034
## 80 3.3745 nan 0.0500 -0.0119
## 100 3.0944 nan 0.0500 -0.0002
## 120 2.8843 nan 0.0500 -0.0151
## 140 2.7255 nan 0.0500 -0.0002
## 160 2.5864 nan 0.0500 -0.0183
## 180 2.4316 nan 0.0500 -0.0128
## 200 2.2901 nan 0.0500 -0.0063
## 220 2.1765 nan 0.0500 -0.0167
## 240 2.0934 nan 0.0500 -0.0136
## 260 2.0053 nan 0.0500 -0.0113
## 280 1.9271 nan 0.0500 -0.0223
## 300 1.8562 nan 0.0500 -0.0181
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.0272 nan 0.1000 7.3389
## 2 46.9954 nan 0.1000 5.7975
## 3 42.0666 nan 0.1000 4.8178
## 4 37.9822 nan 0.1000 4.1102
## 5 34.1106 nan 0.1000 3.7522
## 6 31.0223 nan 0.1000 2.7855
## 7 27.8301 nan 0.1000 2.7865
## 8 25.4326 nan 0.1000 2.3715
## 9 23.6111 nan 0.1000 1.9434
## 10 21.8393 nan 0.1000 1.5203
## 20 11.2842 nan 0.1000 0.4453
## 40 5.8421 nan 0.1000 0.0947
## 60 4.7185 nan 0.1000 0.0019
## 80 4.4229 nan 0.1000 -0.0531
## 100 4.2245 nan 0.1000 -0.0037
## 120 4.0512 nan 0.1000 -0.0104
## 140 3.9244 nan 0.1000 -0.0365
## 160 3.8124 nan 0.1000 -0.0235
## 180 3.7065 nan 0.1000 -0.0178
## 200 3.6129 nan 0.1000 -0.0115
## 220 3.5099 nan 0.1000 -0.0344
## 240 3.4259 nan 0.1000 -0.0262
## 260 3.3584 nan 0.1000 -0.0170
## 280 3.2961 nan 0.1000 -0.0191
## 300 3.2404 nan 0.1000 -0.0209
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.0410 nan 0.1000 6.9696
## 2 46.7956 nan 0.1000 5.8817
## 3 41.3360 nan 0.1000 5.2099
## 4 36.7949 nan 0.1000 3.7840
## 5 33.6464 nan 0.1000 3.1577
## 6 30.4385 nan 0.1000 2.9681
## 7 27.8181 nan 0.1000 2.5351
## 8 24.9692 nan 0.1000 2.3506
## 9 22.7097 nan 0.1000 2.1194
## 10 20.8603 nan 0.1000 1.9698
## 20 11.0603 nan 0.1000 0.5672
## 40 5.5132 nan 0.1000 0.0774
## 60 4.5051 nan 0.1000 0.0077
## 80 4.1886 nan 0.1000 -0.0056
## 100 4.0222 nan 0.1000 -0.0118
## 120 3.8851 nan 0.1000 -0.0125
## 140 3.7724 nan 0.1000 -0.0125
## 160 3.6565 nan 0.1000 -0.0092
## 180 3.5716 nan 0.1000 -0.0101
## 200 3.4841 nan 0.1000 -0.0455
## 220 3.4304 nan 0.1000 -0.0054
## 240 3.3541 nan 0.1000 -0.0091
## 260 3.3104 nan 0.1000 -0.0223
## 280 3.2504 nan 0.1000 -0.0040
## 300 3.2099 nan 0.1000 -0.0253
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.0709 nan 0.1000 7.3321
## 2 47.0443 nan 0.1000 5.4503
## 3 42.0566 nan 0.1000 5.1609
## 4 37.6932 nan 0.1000 4.1360
## 5 33.9424 nan 0.1000 3.7307
## 6 30.0233 nan 0.1000 3.1631
## 7 27.3552 nan 0.1000 2.6061
## 8 24.9600 nan 0.1000 1.9477
## 9 22.6653 nan 0.1000 1.9668
## 10 20.9076 nan 0.1000 1.4590
## 20 11.3000 nan 0.1000 0.2648
## 40 6.1307 nan 0.1000 0.0326
## 60 5.0579 nan 0.1000 0.0327
## 80 4.7267 nan 0.1000 -0.0030
## 100 4.4504 nan 0.1000 -0.0164
## 120 4.3004 nan 0.1000 -0.0130
## 140 4.1323 nan 0.1000 -0.0222
## 160 4.0080 nan 0.1000 -0.0376
## 180 3.9045 nan 0.1000 -0.0196
## 200 3.8153 nan 0.1000 -0.0240
## 220 3.7631 nan 0.1000 -0.0191
## 240 3.6920 nan 0.1000 -0.0248
## 260 3.6260 nan 0.1000 -0.0014
## 280 3.5905 nan 0.1000 -0.0302
## 300 3.5211 nan 0.1000 -0.0181
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.7248 nan 0.1000 9.2858
## 2 43.2063 nan 0.1000 7.5471
## 3 36.5006 nan 0.1000 5.7241
## 4 31.2351 nan 0.1000 4.8840
## 5 26.7152 nan 0.1000 4.2072
## 6 23.0201 nan 0.1000 3.4714
## 7 20.1231 nan 0.1000 2.4880
## 8 17.5469 nan 0.1000 2.6343
## 9 15.6246 nan 0.1000 1.5836
## 10 13.8156 nan 0.1000 1.7091
## 20 5.7140 nan 0.1000 0.3476
## 40 3.4255 nan 0.1000 -0.0247
## 60 2.8808 nan 0.1000 -0.0209
## 80 2.5487 nan 0.1000 -0.0336
## 100 2.2685 nan 0.1000 -0.0374
## 120 2.0651 nan 0.1000 -0.0084
## 140 1.8614 nan 0.1000 -0.0207
## 160 1.7001 nan 0.1000 -0.0309
## 180 1.5659 nan 0.1000 -0.0292
## 200 1.4643 nan 0.1000 -0.0334
## 220 1.3380 nan 0.1000 -0.0175
## 240 1.2432 nan 0.1000 -0.0182
## 260 1.1622 nan 0.1000 -0.0081
## 280 1.1045 nan 0.1000 -0.0181
## 300 1.0439 nan 0.1000 -0.0124
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.4646 nan 0.1000 8.9282
## 2 42.9349 nan 0.1000 7.7266
## 3 36.5747 nan 0.1000 6.5063
## 4 31.2569 nan 0.1000 5.3462
## 5 27.1363 nan 0.1000 4.5185
## 6 23.5290 nan 0.1000 3.6736
## 7 20.5851 nan 0.1000 2.6638
## 8 18.0168 nan 0.1000 1.9615
## 9 15.7701 nan 0.1000 2.1022
## 10 13.9885 nan 0.1000 1.7248
## 20 5.9226 nan 0.1000 0.2677
## 40 3.6319 nan 0.1000 0.0126
## 60 3.0749 nan 0.1000 -0.0128
## 80 2.7207 nan 0.1000 -0.0233
## 100 2.5197 nan 0.1000 -0.0504
## 120 2.3332 nan 0.1000 -0.0350
## 140 2.1537 nan 0.1000 -0.0299
## 160 2.0318 nan 0.1000 -0.0123
## 180 1.9086 nan 0.1000 -0.0406
## 200 1.8073 nan 0.1000 -0.0197
## 220 1.7195 nan 0.1000 -0.0087
## 240 1.6083 nan 0.1000 -0.0175
## 260 1.5153 nan 0.1000 -0.0226
## 280 1.4391 nan 0.1000 -0.0261
## 300 1.3732 nan 0.1000 -0.0259
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.4166 nan 0.1000 8.9886
## 2 42.9857 nan 0.1000 7.5408
## 3 36.7442 nan 0.1000 5.7071
## 4 31.5188 nan 0.1000 5.3673
## 5 27.2102 nan 0.1000 3.8222
## 6 23.7300 nan 0.1000 3.4089
## 7 20.6408 nan 0.1000 2.8187
## 8 18.1909 nan 0.1000 2.4432
## 9 16.0387 nan 0.1000 2.3327
## 10 14.2283 nan 0.1000 1.6718
## 20 6.0296 nan 0.1000 0.2584
## 40 3.8770 nan 0.1000 -0.0702
## 60 3.3455 nan 0.1000 -0.0235
## 80 3.0897 nan 0.1000 -0.0296
## 100 2.8656 nan 0.1000 -0.0167
## 120 2.6807 nan 0.1000 -0.0168
## 140 2.5298 nan 0.1000 -0.0186
## 160 2.4246 nan 0.1000 -0.0132
## 180 2.2800 nan 0.1000 -0.0371
## 200 2.1757 nan 0.1000 -0.0113
## 220 2.0629 nan 0.1000 -0.0145
## 240 1.9731 nan 0.1000 -0.0129
## 260 1.8797 nan 0.1000 -0.0194
## 280 1.7918 nan 0.1000 -0.0259
## 300 1.7220 nan 0.1000 -0.0282
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.0360 nan 0.1000 9.5484
## 2 41.8452 nan 0.1000 7.7112
## 3 35.1514 nan 0.1000 6.1821
## 4 29.7073 nan 0.1000 4.9340
## 5 25.0406 nan 0.1000 4.3463
## 6 21.0266 nan 0.1000 3.5775
## 7 17.9713 nan 0.1000 2.7707
## 8 15.7020 nan 0.1000 2.0902
## 9 13.5466 nan 0.1000 2.1521
## 10 11.8641 nan 0.1000 1.6025
## 20 4.5786 nan 0.1000 0.1816
## 40 2.6642 nan 0.1000 -0.0197
## 60 2.1329 nan 0.1000 -0.0342
## 80 1.7763 nan 0.1000 -0.0151
## 100 1.4969 nan 0.1000 -0.0152
## 120 1.2975 nan 0.1000 -0.0166
## 140 1.1381 nan 0.1000 -0.0213
## 160 0.9880 nan 0.1000 -0.0128
## 180 0.8813 nan 0.1000 -0.0133
## 200 0.7783 nan 0.1000 -0.0059
## 220 0.6910 nan 0.1000 -0.0115
## 240 0.6224 nan 0.1000 -0.0082
## 260 0.5610 nan 0.1000 -0.0076
## 280 0.5034 nan 0.1000 -0.0080
## 300 0.4527 nan 0.1000 -0.0074
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.2778 nan 0.1000 8.9345
## 2 41.7666 nan 0.1000 7.7506
## 3 34.9156 nan 0.1000 6.8147
## 4 29.5401 nan 0.1000 5.6076
## 5 25.0086 nan 0.1000 4.5469
## 6 21.6057 nan 0.1000 3.8313
## 7 18.7128 nan 0.1000 3.0627
## 8 16.1744 nan 0.1000 2.2358
## 9 14.0793 nan 0.1000 2.0603
## 10 12.4610 nan 0.1000 1.1170
## 20 4.8868 nan 0.1000 0.2328
## 40 3.0229 nan 0.1000 -0.0134
## 60 2.5680 nan 0.1000 -0.0368
## 80 2.2054 nan 0.1000 -0.0141
## 100 1.9311 nan 0.1000 -0.0270
## 120 1.7509 nan 0.1000 -0.0236
## 140 1.6078 nan 0.1000 -0.0137
## 160 1.4672 nan 0.1000 -0.0156
## 180 1.3258 nan 0.1000 -0.0185
## 200 1.2247 nan 0.1000 -0.0197
## 220 1.1190 nan 0.1000 -0.0224
## 240 1.0206 nan 0.1000 -0.0170
## 260 0.9396 nan 0.1000 -0.0046
## 280 0.8615 nan 0.1000 -0.0167
## 300 0.7981 nan 0.1000 -0.0155
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.4441 nan 0.1000 8.8635
## 2 41.9766 nan 0.1000 7.6464
## 3 35.3747 nan 0.1000 6.3424
## 4 29.7385 nan 0.1000 5.3254
## 5 25.3737 nan 0.1000 4.7341
## 6 21.7400 nan 0.1000 3.3840
## 7 18.6101 nan 0.1000 2.9630
## 8 16.2892 nan 0.1000 2.2019
## 9 14.0839 nan 0.1000 2.0074
## 10 12.3625 nan 0.1000 1.5485
## 20 5.4973 nan 0.1000 0.1885
## 40 3.5820 nan 0.1000 -0.0063
## 60 3.1146 nan 0.1000 -0.0166
## 80 2.7698 nan 0.1000 -0.0601
## 100 2.4958 nan 0.1000 -0.0410
## 120 2.2761 nan 0.1000 -0.0009
## 140 2.0761 nan 0.1000 -0.0264
## 160 1.9385 nan 0.1000 -0.0237
## 180 1.7833 nan 0.1000 -0.0195
## 200 1.6533 nan 0.1000 -0.0340
## 220 1.5265 nan 0.1000 -0.0185
## 240 1.4255 nan 0.1000 -0.0151
## 260 1.3589 nan 0.1000 -0.0171
## 280 1.2813 nan 0.1000 -0.0090
## 300 1.2163 nan 0.1000 -0.0220
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.2471 nan 0.0100 0.8171
## 2 60.4397 nan 0.0100 0.7450
## 3 59.7327 nan 0.0100 0.6858
## 4 59.0314 nan 0.0100 0.7008
## 5 58.3337 nan 0.0100 0.7113
## 6 57.6094 nan 0.0100 0.7210
## 7 56.9120 nan 0.0100 0.6757
## 8 56.2622 nan 0.0100 0.7056
## 9 55.5959 nan 0.0100 0.6775
## 10 54.9864 nan 0.0100 0.6378
## 20 48.8743 nan 0.0100 0.5066
## 40 39.3633 nan 0.0100 0.3880
## 60 32.1468 nan 0.0100 0.3041
## 80 26.8350 nan 0.0100 0.2220
## 100 22.6433 nan 0.0100 0.1638
## 120 19.5177 nan 0.0100 0.1366
## 140 16.9959 nan 0.0100 0.1089
## 160 14.9314 nan 0.0100 0.0922
## 180 13.3013 nan 0.0100 0.0675
## 200 11.8810 nan 0.0100 0.0592
## 220 10.7510 nan 0.0100 0.0419
## 240 9.8464 nan 0.0100 0.0284
## 260 9.0426 nan 0.0100 0.0322
## 280 8.3754 nan 0.0100 0.0153
## 300 7.8088 nan 0.0100 0.0213
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.2335 nan 0.0100 0.7930
## 2 60.4126 nan 0.0100 0.7724
## 3 59.7121 nan 0.0100 0.7442
## 4 58.9596 nan 0.0100 0.7410
## 5 58.3147 nan 0.0100 0.6368
## 6 57.6453 nan 0.0100 0.7391
## 7 56.9562 nan 0.0100 0.6340
## 8 56.2538 nan 0.0100 0.6613
## 9 55.6347 nan 0.0100 0.6316
## 10 54.9292 nan 0.0100 0.6321
## 20 48.9372 nan 0.0100 0.5727
## 40 39.3745 nan 0.0100 0.3718
## 60 32.2644 nan 0.0100 0.3136
## 80 27.0154 nan 0.0100 0.2265
## 100 22.8407 nan 0.0100 0.1654
## 120 19.6301 nan 0.0100 0.1356
## 140 17.1105 nan 0.0100 0.1159
## 160 15.1062 nan 0.0100 0.0714
## 180 13.4058 nan 0.0100 0.0683
## 200 12.0099 nan 0.0100 0.0494
## 220 10.8768 nan 0.0100 0.0423
## 240 9.9340 nan 0.0100 0.0393
## 260 9.1577 nan 0.0100 0.0235
## 280 8.4650 nan 0.0100 0.0267
## 300 7.8962 nan 0.0100 0.0230
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.2664 nan 0.0100 0.7512
## 2 60.4755 nan 0.0100 0.7611
## 3 59.7056 nan 0.0100 0.7099
## 4 58.9449 nan 0.0100 0.7729
## 5 58.1742 nan 0.0100 0.7191
## 6 57.4567 nan 0.0100 0.6564
## 7 56.7102 nan 0.0100 0.6633
## 8 56.0161 nan 0.0100 0.6412
## 9 55.3484 nan 0.0100 0.6663
## 10 54.7126 nan 0.0100 0.6485
## 20 48.7478 nan 0.0100 0.5185
## 40 39.5092 nan 0.0100 0.3679
## 60 32.4741 nan 0.0100 0.3224
## 80 26.9540 nan 0.0100 0.1804
## 100 22.8327 nan 0.0100 0.1534
## 120 19.5640 nan 0.0100 0.1331
## 140 17.0115 nan 0.0100 0.0998
## 160 15.0070 nan 0.0100 0.0783
## 180 13.3386 nan 0.0100 0.0634
## 200 12.0034 nan 0.0100 0.0506
## 220 10.8655 nan 0.0100 0.0446
## 240 9.9849 nan 0.0100 0.0375
## 260 9.1796 nan 0.0100 0.0233
## 280 8.5283 nan 0.0100 0.0246
## 300 7.9556 nan 0.0100 0.0140
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.0756 nan 0.0100 1.0436
## 2 60.1121 nan 0.0100 0.9241
## 3 59.1398 nan 0.0100 0.8767
## 4 58.2601 nan 0.0100 0.9505
## 5 57.2728 nan 0.0100 0.8786
## 6 56.3825 nan 0.0100 0.8443
## 7 55.4643 nan 0.0100 0.9856
## 8 54.5975 nan 0.0100 0.9066
## 9 53.7411 nan 0.0100 0.9248
## 10 52.9454 nan 0.0100 0.8292
## 20 45.2351 nan 0.0100 0.6602
## 40 33.4845 nan 0.0100 0.4471
## 60 25.2464 nan 0.0100 0.3424
## 80 19.4439 nan 0.0100 0.2407
## 100 15.3330 nan 0.0100 0.1572
## 120 12.3388 nan 0.0100 0.0974
## 140 10.1592 nan 0.0100 0.0789
## 160 8.5966 nan 0.0100 0.0510
## 180 7.4095 nan 0.0100 0.0269
## 200 6.5071 nan 0.0100 0.0290
## 220 5.8273 nan 0.0100 0.0229
## 240 5.3414 nan 0.0100 0.0138
## 260 4.9522 nan 0.0100 0.0146
## 280 4.6608 nan 0.0100 0.0052
## 300 4.4169 nan 0.0100 0.0032
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.0249 nan 0.0100 1.0083
## 2 60.0501 nan 0.0100 0.8580
## 3 59.0255 nan 0.0100 1.0498
## 4 58.0567 nan 0.0100 0.8689
## 5 57.1328 nan 0.0100 0.9212
## 6 56.2389 nan 0.0100 0.9170
## 7 55.3481 nan 0.0100 0.8473
## 8 54.4487 nan 0.0100 0.8020
## 9 53.5667 nan 0.0100 0.7988
## 10 52.7073 nan 0.0100 0.7873
## 20 45.0638 nan 0.0100 0.6397
## 40 33.1983 nan 0.0100 0.4709
## 60 25.0138 nan 0.0100 0.3496
## 80 19.3344 nan 0.0100 0.2313
## 100 15.2786 nan 0.0100 0.1473
## 120 12.3923 nan 0.0100 0.1116
## 140 10.2626 nan 0.0100 0.0945
## 160 8.6657 nan 0.0100 0.0637
## 180 7.4821 nan 0.0100 0.0394
## 200 6.5980 nan 0.0100 0.0290
## 220 5.9308 nan 0.0100 0.0250
## 240 5.4515 nan 0.0100 0.0131
## 260 5.0887 nan 0.0100 0.0133
## 280 4.8044 nan 0.0100 0.0117
## 300 4.5858 nan 0.0100 0.0042
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.0146 nan 0.0100 1.1238
## 2 60.0157 nan 0.0100 1.1308
## 3 59.0649 nan 0.0100 1.0358
## 4 58.0879 nan 0.0100 0.8187
## 5 57.2114 nan 0.0100 0.8622
## 6 56.2606 nan 0.0100 0.9823
## 7 55.3185 nan 0.0100 0.8737
## 8 54.4709 nan 0.0100 0.8420
## 9 53.5584 nan 0.0100 0.8816
## 10 52.7162 nan 0.0100 0.8105
## 20 45.0614 nan 0.0100 0.6640
## 40 33.2360 nan 0.0100 0.4679
## 60 25.1540 nan 0.0100 0.3489
## 80 19.4696 nan 0.0100 0.2371
## 100 15.3101 nan 0.0100 0.1908
## 120 12.4223 nan 0.0100 0.1178
## 140 10.3664 nan 0.0100 0.0680
## 160 8.8373 nan 0.0100 0.0644
## 180 7.6617 nan 0.0100 0.0468
## 200 6.8181 nan 0.0100 0.0314
## 220 6.1964 nan 0.0100 0.0243
## 240 5.6959 nan 0.0100 0.0157
## 260 5.3067 nan 0.0100 0.0114
## 280 5.0145 nan 0.0100 0.0126
## 300 4.8007 nan 0.0100 0.0003
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.0165 nan 0.0100 1.0477
## 2 59.9716 nan 0.0100 1.0479
## 3 58.9321 nan 0.0100 1.0314
## 4 57.9297 nan 0.0100 1.0420
## 5 56.8560 nan 0.0100 1.0030
## 6 55.8343 nan 0.0100 1.1082
## 7 54.9082 nan 0.0100 0.9856
## 8 53.9656 nan 0.0100 0.8256
## 9 53.0861 nan 0.0100 0.8650
## 10 52.1747 nan 0.0100 0.8640
## 20 44.1799 nan 0.0100 0.7290
## 40 31.8072 nan 0.0100 0.5093
## 60 23.4430 nan 0.0100 0.3150
## 80 17.5244 nan 0.0100 0.2309
## 100 13.4227 nan 0.0100 0.1642
## 120 10.5454 nan 0.0100 0.1007
## 140 8.5015 nan 0.0100 0.0850
## 160 7.0931 nan 0.0100 0.0511
## 180 6.0581 nan 0.0100 0.0393
## 200 5.3591 nan 0.0100 0.0243
## 220 4.8015 nan 0.0100 0.0149
## 240 4.4036 nan 0.0100 0.0094
## 260 4.0873 nan 0.0100 0.0070
## 280 3.8529 nan 0.0100 -0.0023
## 300 3.6627 nan 0.0100 -0.0017
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.0055 nan 0.0100 1.0640
## 2 59.9550 nan 0.0100 1.0846
## 3 58.9050 nan 0.0100 1.0082
## 4 57.8709 nan 0.0100 0.9601
## 5 56.8651 nan 0.0100 1.0693
## 6 55.8442 nan 0.0100 0.9167
## 7 54.8700 nan 0.0100 0.8096
## 8 53.9720 nan 0.0100 0.8210
## 9 53.0454 nan 0.0100 0.8885
## 10 52.1660 nan 0.0100 0.8323
## 20 44.0084 nan 0.0100 0.6535
## 40 31.7890 nan 0.0100 0.5025
## 60 23.3379 nan 0.0100 0.3161
## 80 17.6059 nan 0.0100 0.2064
## 100 13.5621 nan 0.0100 0.1660
## 120 10.6917 nan 0.0100 0.1144
## 140 8.7166 nan 0.0100 0.0837
## 160 7.3243 nan 0.0100 0.0543
## 180 6.3081 nan 0.0100 0.0295
## 200 5.5555 nan 0.0100 0.0215
## 220 5.0228 nan 0.0100 0.0170
## 240 4.6105 nan 0.0100 0.0076
## 260 4.3088 nan 0.0100 0.0008
## 280 4.0892 nan 0.0100 0.0038
## 300 3.9008 nan 0.0100 -0.0003
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.9951 nan 0.0100 0.9559
## 2 59.9393 nan 0.0100 0.9891
## 3 58.8988 nan 0.0100 0.9849
## 4 57.8277 nan 0.0100 1.1098
## 5 56.8630 nan 0.0100 1.0348
## 6 55.8554 nan 0.0100 0.9254
## 7 54.9125 nan 0.0100 0.9681
## 8 53.9700 nan 0.0100 0.7811
## 9 53.1000 nan 0.0100 0.8305
## 10 52.2293 nan 0.0100 0.8243
## 20 44.1124 nan 0.0100 0.7188
## 40 31.9684 nan 0.0100 0.4830
## 60 23.5360 nan 0.0100 0.3569
## 80 17.7766 nan 0.0100 0.2328
## 100 13.7659 nan 0.0100 0.1549
## 120 10.9368 nan 0.0100 0.1074
## 140 8.9749 nan 0.0100 0.0610
## 160 7.5791 nan 0.0100 0.0477
## 180 6.5736 nan 0.0100 0.0359
## 200 5.8515 nan 0.0100 0.0259
## 220 5.3452 nan 0.0100 0.0110
## 240 4.9590 nan 0.0100 0.0148
## 260 4.6770 nan 0.0100 0.0030
## 280 4.4678 nan 0.0100 0.0046
## 300 4.2982 nan 0.0100 0.0022
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.2510 nan 0.0500 3.8659
## 2 54.9483 nan 0.0500 3.2659
## 3 51.7792 nan 0.0500 3.0892
## 4 48.8502 nan 0.0500 2.9909
## 5 46.2271 nan 0.0500 2.5995
## 6 43.8243 nan 0.0500 2.3293
## 7 41.3860 nan 0.0500 2.3403
## 8 39.3449 nan 0.0500 2.0760
## 9 37.2151 nan 0.0500 2.0170
## 10 35.4728 nan 0.0500 1.5757
## 20 23.2106 nan 0.0500 0.8434
## 40 12.2243 nan 0.0500 0.2998
## 60 7.9661 nan 0.0500 0.1322
## 80 6.0688 nan 0.0500 -0.0020
## 100 5.1843 nan 0.0500 0.0107
## 120 4.7871 nan 0.0500 0.0097
## 140 4.6039 nan 0.0500 -0.0185
## 160 4.4874 nan 0.0500 -0.0098
## 180 4.3956 nan 0.0500 -0.0054
## 200 4.3290 nan 0.0500 -0.0157
## 220 4.2400 nan 0.0500 -0.0015
## 240 4.1610 nan 0.0500 -0.0035
## 260 4.0935 nan 0.0500 -0.0111
## 280 4.0350 nan 0.0500 -0.0390
## 300 3.9945 nan 0.0500 -0.0038
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.3960 nan 0.0500 3.8379
## 2 54.9671 nan 0.0500 3.4623
## 3 51.4929 nan 0.0500 3.0140
## 4 48.6050 nan 0.0500 2.8483
## 5 45.9079 nan 0.0500 2.4888
## 6 43.7300 nan 0.0500 2.2019
## 7 41.0662 nan 0.0500 2.1655
## 8 38.7998 nan 0.0500 1.8705
## 9 36.9989 nan 0.0500 1.8070
## 10 35.3301 nan 0.0500 1.7297
## 20 22.1881 nan 0.0500 0.8367
## 40 11.8501 nan 0.0500 0.1725
## 60 7.7668 nan 0.0500 0.1199
## 80 6.0438 nan 0.0500 0.0432
## 100 5.2471 nan 0.0500 -0.0102
## 120 4.8393 nan 0.0500 -0.0134
## 140 4.6629 nan 0.0500 -0.0027
## 160 4.5407 nan 0.0500 -0.0091
## 180 4.4433 nan 0.0500 -0.0095
## 200 4.3598 nan 0.0500 -0.0017
## 220 4.3072 nan 0.0500 0.0003
## 240 4.2494 nan 0.0500 -0.0223
## 260 4.1912 nan 0.0500 -0.0101
## 280 4.1356 nan 0.0500 -0.0086
## 300 4.0780 nan 0.0500 -0.0042
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.0784 nan 0.0500 4.1658
## 2 54.5911 nan 0.0500 3.2199
## 3 51.4127 nan 0.0500 3.0460
## 4 48.4753 nan 0.0500 2.9579
## 5 45.7399 nan 0.0500 2.4592
## 6 43.2509 nan 0.0500 2.4544
## 7 41.0372 nan 0.0500 2.1524
## 8 39.1829 nan 0.0500 1.9616
## 9 37.3444 nan 0.0500 1.7148
## 10 35.6296 nan 0.0500 1.7590
## 20 22.9508 nan 0.0500 0.5356
## 40 11.7975 nan 0.0500 0.2155
## 60 8.0006 nan 0.0500 0.0976
## 80 6.1805 nan 0.0500 0.0126
## 100 5.3689 nan 0.0500 -0.0095
## 120 5.0349 nan 0.0500 -0.0294
## 140 4.8268 nan 0.0500 -0.0081
## 160 4.7045 nan 0.0500 -0.0124
## 180 4.6158 nan 0.0500 -0.0149
## 200 4.5387 nan 0.0500 -0.0047
## 220 4.4880 nan 0.0500 -0.0078
## 240 4.4157 nan 0.0500 -0.0124
## 260 4.3487 nan 0.0500 -0.0098
## 280 4.3108 nan 0.0500 -0.0146
## 300 4.2531 nan 0.0500 -0.0117
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.0509 nan 0.0500 4.9164
## 2 52.6697 nan 0.0500 4.1244
## 3 48.5121 nan 0.0500 3.7657
## 4 44.9575 nan 0.0500 3.6333
## 5 41.7553 nan 0.0500 3.3531
## 6 38.5213 nan 0.0500 3.1923
## 7 35.5831 nan 0.0500 3.0295
## 8 33.0286 nan 0.0500 2.5774
## 9 30.6986 nan 0.0500 2.1684
## 10 28.7020 nan 0.0500 2.1467
## 20 15.1303 nan 0.0500 0.8677
## 40 6.5465 nan 0.0500 0.1700
## 60 4.4643 nan 0.0500 0.0398
## 80 3.7855 nan 0.0500 -0.0092
## 100 3.3733 nan 0.0500 -0.0265
## 120 3.1284 nan 0.0500 -0.0170
## 140 2.9125 nan 0.0500 -0.0167
## 160 2.7485 nan 0.0500 -0.0306
## 180 2.5813 nan 0.0500 -0.0099
## 200 2.4451 nan 0.0500 -0.0085
## 220 2.3395 nan 0.0500 -0.0169
## 240 2.2064 nan 0.0500 -0.0071
## 260 2.1134 nan 0.0500 -0.0148
## 280 2.0193 nan 0.0500 -0.0121
## 300 1.9274 nan 0.0500 -0.0259
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.9425 nan 0.0500 5.1620
## 2 52.3220 nan 0.0500 4.1111
## 3 48.0163 nan 0.0500 3.8190
## 4 44.2704 nan 0.0500 3.5705
## 5 40.9959 nan 0.0500 3.4008
## 6 37.8627 nan 0.0500 2.8173
## 7 35.1591 nan 0.0500 2.6522
## 8 32.5420 nan 0.0500 2.3474
## 9 30.0371 nan 0.0500 2.2974
## 10 27.8753 nan 0.0500 1.7058
## 20 14.6966 nan 0.0500 0.8123
## 40 6.5102 nan 0.0500 0.1154
## 60 4.5803 nan 0.0500 0.0179
## 80 3.9867 nan 0.0500 -0.0312
## 100 3.6925 nan 0.0500 -0.0373
## 120 3.4431 nan 0.0500 -0.0100
## 140 3.2715 nan 0.0500 -0.0312
## 160 3.1237 nan 0.0500 -0.0117
## 180 2.9960 nan 0.0500 -0.0188
## 200 2.9043 nan 0.0500 -0.0278
## 220 2.7744 nan 0.0500 -0.0157
## 240 2.6667 nan 0.0500 -0.0303
## 260 2.5674 nan 0.0500 -0.0095
## 280 2.4871 nan 0.0500 -0.0188
## 300 2.3901 nan 0.0500 -0.0260
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.0138 nan 0.0500 4.8806
## 2 52.4046 nan 0.0500 4.3052
## 3 48.1771 nan 0.0500 4.0895
## 4 44.6355 nan 0.0500 3.7533
## 5 41.2741 nan 0.0500 2.9681
## 6 38.2607 nan 0.0500 2.8724
## 7 35.4450 nan 0.0500 2.7316
## 8 32.8851 nan 0.0500 2.1201
## 9 30.5620 nan 0.0500 2.0669
## 10 28.5482 nan 0.0500 1.9160
## 20 15.4266 nan 0.0500 0.7355
## 40 6.7283 nan 0.0500 0.1622
## 60 4.7553 nan 0.0500 -0.0083
## 80 4.1564 nan 0.0500 -0.0050
## 100 3.8614 nan 0.0500 -0.0238
## 120 3.6413 nan 0.0500 -0.0073
## 140 3.5058 nan 0.0500 -0.0266
## 160 3.3426 nan 0.0500 -0.0188
## 180 3.2226 nan 0.0500 -0.0181
## 200 3.1103 nan 0.0500 -0.0129
## 220 3.0073 nan 0.0500 -0.0249
## 240 2.9350 nan 0.0500 -0.0097
## 260 2.8251 nan 0.0500 -0.0103
## 280 2.7574 nan 0.0500 -0.0075
## 300 2.6761 nan 0.0500 -0.0163
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.8288 nan 0.0500 5.2259
## 2 52.1941 nan 0.0500 4.6416
## 3 48.1045 nan 0.0500 4.0980
## 4 44.0999 nan 0.0500 3.3660
## 5 40.4685 nan 0.0500 3.2812
## 6 37.2454 nan 0.0500 3.6083
## 7 34.1335 nan 0.0500 2.8631
## 8 31.5240 nan 0.0500 2.4587
## 9 29.1742 nan 0.0500 2.1428
## 10 26.8730 nan 0.0500 2.1001
## 20 13.4004 nan 0.0500 0.8190
## 40 5.2974 nan 0.0500 0.1081
## 60 3.6409 nan 0.0500 -0.0084
## 80 3.0458 nan 0.0500 -0.0107
## 100 2.7261 nan 0.0500 -0.0317
## 120 2.4446 nan 0.0500 -0.0325
## 140 2.2115 nan 0.0500 -0.0162
## 160 1.9837 nan 0.0500 -0.0142
## 180 1.8272 nan 0.0500 -0.0178
## 200 1.6830 nan 0.0500 -0.0170
## 220 1.5669 nan 0.0500 -0.0166
## 240 1.4746 nan 0.0500 -0.0131
## 260 1.3603 nan 0.0500 -0.0207
## 280 1.2661 nan 0.0500 -0.0174
## 300 1.1680 nan 0.0500 -0.0056
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.8123 nan 0.0500 5.3557
## 2 51.8963 nan 0.0500 4.3317
## 3 47.6316 nan 0.0500 4.3542
## 4 43.8881 nan 0.0500 3.9939
## 5 40.4536 nan 0.0500 3.7371
## 6 37.3856 nan 0.0500 2.9395
## 7 34.5803 nan 0.0500 3.0939
## 8 31.7217 nan 0.0500 2.3956
## 9 29.3008 nan 0.0500 2.3267
## 10 27.2113 nan 0.0500 2.0625
## 20 13.3472 nan 0.0500 0.8152
## 40 5.4916 nan 0.0500 0.1057
## 60 3.9257 nan 0.0500 0.0078
## 80 3.3900 nan 0.0500 -0.0255
## 100 3.0654 nan 0.0500 -0.0153
## 120 2.7759 nan 0.0500 -0.0105
## 140 2.6206 nan 0.0500 -0.0235
## 160 2.4814 nan 0.0500 -0.0235
## 180 2.3341 nan 0.0500 -0.0282
## 200 2.1656 nan 0.0500 -0.0143
## 220 2.0448 nan 0.0500 -0.0162
## 240 1.9491 nan 0.0500 -0.0229
## 260 1.8512 nan 0.0500 -0.0216
## 280 1.7562 nan 0.0500 -0.0140
## 300 1.6646 nan 0.0500 -0.0048
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.7805 nan 0.0500 5.1416
## 2 52.0776 nan 0.0500 4.5647
## 3 47.6773 nan 0.0500 4.3866
## 4 43.7995 nan 0.0500 4.0749
## 5 40.1579 nan 0.0500 3.3395
## 6 36.9355 nan 0.0500 3.0672
## 7 33.9534 nan 0.0500 2.8057
## 8 31.3132 nan 0.0500 2.2860
## 9 28.8416 nan 0.0500 2.1411
## 10 26.7253 nan 0.0500 1.8991
## 20 13.3205 nan 0.0500 0.7813
## 40 5.7091 nan 0.0500 0.1037
## 60 4.2640 nan 0.0500 -0.0049
## 80 3.8119 nan 0.0500 -0.0376
## 100 3.5128 nan 0.0500 -0.0240
## 120 3.3067 nan 0.0500 -0.0220
## 140 3.1178 nan 0.0500 -0.0032
## 160 2.9730 nan 0.0500 -0.0221
## 180 2.8217 nan 0.0500 -0.0100
## 200 2.6794 nan 0.0500 -0.0087
## 220 2.5736 nan 0.0500 -0.0121
## 240 2.4393 nan 0.0500 -0.0119
## 260 2.3314 nan 0.0500 -0.0181
## 280 2.2384 nan 0.0500 -0.0124
## 300 2.1517 nan 0.0500 -0.0140
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.9715 nan 0.1000 7.0198
## 2 48.7284 nan 0.1000 6.0682
## 3 43.0850 nan 0.1000 4.6082
## 4 38.6990 nan 0.1000 4.1440
## 5 34.9746 nan 0.1000 3.3631
## 6 31.3431 nan 0.1000 3.2554
## 7 28.5754 nan 0.1000 2.5183
## 8 25.8712 nan 0.1000 2.0595
## 9 24.1180 nan 0.1000 1.6862
## 10 22.5115 nan 0.1000 1.5865
## 20 11.7230 nan 0.1000 0.4389
## 40 6.0035 nan 0.1000 -0.0010
## 60 4.8913 nan 0.1000 0.0024
## 80 4.6013 nan 0.1000 -0.0361
## 100 4.4268 nan 0.1000 -0.0313
## 120 4.2823 nan 0.1000 -0.0175
## 140 4.1785 nan 0.1000 -0.0100
## 160 4.0911 nan 0.1000 -0.0423
## 180 4.0082 nan 0.1000 -0.0567
## 200 3.9056 nan 0.1000 -0.0120
## 220 3.8073 nan 0.1000 -0.0044
## 240 3.7222 nan 0.1000 -0.0130
## 260 3.6461 nan 0.1000 -0.0072
## 280 3.5788 nan 0.1000 -0.0369
## 300 3.5061 nan 0.1000 -0.0205
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.4538 nan 0.1000 7.4325
## 2 48.5028 nan 0.1000 6.1401
## 3 43.8059 nan 0.1000 4.9498
## 4 39.0279 nan 0.1000 4.3574
## 5 35.4411 nan 0.1000 3.8483
## 6 32.2688 nan 0.1000 3.2946
## 7 29.3795 nan 0.1000 2.8381
## 8 27.0367 nan 0.1000 1.9143
## 9 24.5248 nan 0.1000 2.0962
## 10 22.5315 nan 0.1000 1.9347
## 20 11.8805 nan 0.1000 0.6654
## 40 6.0573 nan 0.1000 0.0025
## 60 4.9301 nan 0.1000 -0.0363
## 80 4.6375 nan 0.1000 0.0014
## 100 4.4643 nan 0.1000 -0.0295
## 120 4.3344 nan 0.1000 -0.0318
## 140 4.2078 nan 0.1000 -0.0197
## 160 4.0812 nan 0.1000 -0.0163
## 180 3.9932 nan 0.1000 -0.0458
## 200 3.8917 nan 0.1000 -0.0093
## 220 3.8157 nan 0.1000 -0.0315
## 240 3.7674 nan 0.1000 -0.0250
## 260 3.6985 nan 0.1000 -0.0056
## 280 3.6336 nan 0.1000 0.0005
## 300 3.5467 nan 0.1000 -0.0326
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.8076 nan 0.1000 7.1677
## 2 49.1246 nan 0.1000 5.8667
## 3 44.3561 nan 0.1000 4.3528
## 4 39.8620 nan 0.1000 4.4319
## 5 35.5466 nan 0.1000 4.4480
## 6 32.0148 nan 0.1000 2.8939
## 7 29.2254 nan 0.1000 2.6131
## 8 26.4858 nan 0.1000 2.3813
## 9 24.1384 nan 0.1000 2.1010
## 10 22.1882 nan 0.1000 1.9119
## 20 11.7862 nan 0.1000 0.5451
## 40 6.1845 nan 0.1000 0.0485
## 60 5.1008 nan 0.1000 -0.0325
## 80 4.8234 nan 0.1000 0.0120
## 100 4.5990 nan 0.1000 -0.0033
## 120 4.4453 nan 0.1000 -0.0131
## 140 4.3541 nan 0.1000 -0.0176
## 160 4.2599 nan 0.1000 -0.0030
## 180 4.1840 nan 0.1000 -0.0513
## 200 4.0914 nan 0.1000 -0.0424
## 220 4.0280 nan 0.1000 -0.0460
## 240 3.9639 nan 0.1000 -0.0059
## 260 3.8893 nan 0.1000 -0.0062
## 280 3.8233 nan 0.1000 -0.0216
## 300 3.7812 nan 0.1000 -0.0077
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.4696 nan 0.1000 10.0387
## 2 44.0876 nan 0.1000 8.4219
## 3 37.4773 nan 0.1000 5.6257
## 4 32.0671 nan 0.1000 4.8524
## 5 27.5576 nan 0.1000 3.3977
## 6 23.7786 nan 0.1000 3.5954
## 7 20.7378 nan 0.1000 3.0122
## 8 18.2250 nan 0.1000 2.3834
## 9 16.2712 nan 0.1000 1.8746
## 10 14.3526 nan 0.1000 1.6939
## 20 6.2810 nan 0.1000 0.3110
## 40 3.9163 nan 0.1000 -0.0380
## 60 3.2436 nan 0.1000 -0.0344
## 80 2.8314 nan 0.1000 -0.0247
## 100 2.5282 nan 0.1000 -0.0115
## 120 2.2960 nan 0.1000 -0.0135
## 140 2.0580 nan 0.1000 -0.0416
## 160 1.8536 nan 0.1000 -0.0102
## 180 1.7043 nan 0.1000 -0.0245
## 200 1.5893 nan 0.1000 -0.0380
## 220 1.4785 nan 0.1000 -0.0197
## 240 1.3549 nan 0.1000 -0.0197
## 260 1.2608 nan 0.1000 -0.0142
## 280 1.1749 nan 0.1000 -0.0086
## 300 1.1093 nan 0.1000 -0.0141
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.3949 nan 0.1000 9.6151
## 2 44.4557 nan 0.1000 7.8735
## 3 37.9398 nan 0.1000 6.1136
## 4 32.4552 nan 0.1000 5.5608
## 5 27.8174 nan 0.1000 4.0227
## 6 24.2423 nan 0.1000 3.7077
## 7 21.3314 nan 0.1000 2.9468
## 8 18.7740 nan 0.1000 2.2866
## 9 16.8861 nan 0.1000 2.0097
## 10 15.2661 nan 0.1000 1.6202
## 20 6.6689 nan 0.1000 0.1888
## 40 4.1151 nan 0.1000 -0.0398
## 60 3.5551 nan 0.1000 -0.0256
## 80 3.2555 nan 0.1000 -0.0548
## 100 2.9841 nan 0.1000 -0.0295
## 120 2.7619 nan 0.1000 -0.0303
## 140 2.5536 nan 0.1000 -0.0316
## 160 2.4049 nan 0.1000 -0.0079
## 180 2.2955 nan 0.1000 -0.0522
## 200 2.1531 nan 0.1000 -0.0189
## 220 2.0583 nan 0.1000 -0.0273
## 240 1.9377 nan 0.1000 -0.0362
## 260 1.8371 nan 0.1000 -0.0132
## 280 1.7450 nan 0.1000 -0.0195
## 300 1.6536 nan 0.1000 -0.0239
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.7991 nan 0.1000 9.0010
## 2 44.8187 nan 0.1000 8.0573
## 3 38.1452 nan 0.1000 7.1665
## 4 32.6997 nan 0.1000 5.5486
## 5 28.1284 nan 0.1000 4.4632
## 6 24.3199 nan 0.1000 3.3914
## 7 20.9843 nan 0.1000 3.1358
## 8 18.3428 nan 0.1000 2.4338
## 9 16.4125 nan 0.1000 2.0159
## 10 14.4656 nan 0.1000 1.4699
## 20 6.7510 nan 0.1000 0.3631
## 40 4.2672 nan 0.1000 -0.0018
## 60 3.7883 nan 0.1000 -0.0640
## 80 3.4441 nan 0.1000 -0.0236
## 100 3.2037 nan 0.1000 -0.0341
## 120 3.0127 nan 0.1000 -0.0426
## 140 2.7514 nan 0.1000 -0.0181
## 160 2.6241 nan 0.1000 -0.0175
## 180 2.4976 nan 0.1000 -0.0186
## 200 2.3551 nan 0.1000 -0.0225
## 220 2.2428 nan 0.1000 -0.0187
## 240 2.1528 nan 0.1000 -0.0362
## 260 2.0643 nan 0.1000 -0.0261
## 280 1.9576 nan 0.1000 -0.0155
## 300 1.8813 nan 0.1000 -0.0339
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.1119 nan 0.1000 10.1801
## 2 43.9556 nan 0.1000 8.0204
## 3 36.7805 nan 0.1000 6.9267
## 4 31.3349 nan 0.1000 5.4869
## 5 26.8050 nan 0.1000 4.8037
## 6 22.9288 nan 0.1000 3.3492
## 7 19.8097 nan 0.1000 3.2726
## 8 17.2449 nan 0.1000 2.4774
## 9 14.7408 nan 0.1000 2.1059
## 10 12.8890 nan 0.1000 1.6024
## 20 5.2312 nan 0.1000 0.1668
## 40 3.0627 nan 0.1000 -0.0497
## 60 2.4368 nan 0.1000 -0.0341
## 80 2.0174 nan 0.1000 -0.0186
## 100 1.7268 nan 0.1000 -0.0274
## 120 1.5123 nan 0.1000 -0.0335
## 140 1.3280 nan 0.1000 -0.0188
## 160 1.1530 nan 0.1000 -0.0235
## 180 1.0248 nan 0.1000 -0.0078
## 200 0.9313 nan 0.1000 -0.0153
## 220 0.8278 nan 0.1000 -0.0149
## 240 0.7259 nan 0.1000 -0.0149
## 260 0.6464 nan 0.1000 -0.0102
## 280 0.5767 nan 0.1000 -0.0119
## 300 0.5298 nan 0.1000 -0.0115
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.0656 nan 0.1000 10.1341
## 2 43.9084 nan 0.1000 7.9801
## 3 37.0762 nan 0.1000 7.0906
## 4 31.0623 nan 0.1000 5.5155
## 5 26.2915 nan 0.1000 3.9790
## 6 22.6846 nan 0.1000 3.2854
## 7 19.5807 nan 0.1000 2.8688
## 8 16.9771 nan 0.1000 2.5670
## 9 14.7056 nan 0.1000 2.1748
## 10 13.0735 nan 0.1000 1.6088
## 20 5.4294 nan 0.1000 0.2670
## 40 3.4078 nan 0.1000 -0.0356
## 60 2.9168 nan 0.1000 -0.0378
## 80 2.5150 nan 0.1000 -0.0234
## 100 2.2520 nan 0.1000 -0.0204
## 120 2.0312 nan 0.1000 -0.0364
## 140 1.7791 nan 0.1000 -0.0303
## 160 1.6362 nan 0.1000 -0.0350
## 180 1.4804 nan 0.1000 -0.0207
## 200 1.3572 nan 0.1000 -0.0084
## 220 1.2256 nan 0.1000 -0.0153
## 240 1.1251 nan 0.1000 -0.0110
## 260 1.0616 nan 0.1000 -0.0150
## 280 0.9769 nan 0.1000 -0.0152
## 300 0.9174 nan 0.1000 -0.0153
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.7891 nan 0.1000 9.8440
## 2 43.3241 nan 0.1000 7.7736
## 3 36.4685 nan 0.1000 6.4494
## 4 30.7599 nan 0.1000 5.5524
## 5 26.1759 nan 0.1000 4.5273
## 6 22.3005 nan 0.1000 3.4149
## 7 19.2184 nan 0.1000 2.9957
## 8 16.7347 nan 0.1000 2.5685
## 9 14.5609 nan 0.1000 1.7038
## 10 12.9032 nan 0.1000 1.6362
## 20 5.6709 nan 0.1000 0.2826
## 40 3.8271 nan 0.1000 -0.0562
## 60 3.3528 nan 0.1000 -0.0259
## 80 3.0576 nan 0.1000 -0.0262
## 100 2.7595 nan 0.1000 -0.0389
## 120 2.4658 nan 0.1000 -0.0333
## 140 2.2557 nan 0.1000 -0.0554
## 160 2.0816 nan 0.1000 -0.0224
## 180 1.9530 nan 0.1000 -0.0394
## 200 1.7937 nan 0.1000 -0.0564
## 220 1.6793 nan 0.1000 -0.0134
## 240 1.5793 nan 0.1000 -0.0152
## 260 1.4946 nan 0.1000 -0.0225
## 280 1.4172 nan 0.1000 -0.0181
## 300 1.3446 nan 0.1000 -0.0199
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.2761 nan 0.0100 0.8008
## 2 59.5575 nan 0.0100 0.7360
## 3 58.7857 nan 0.0100 0.7765
## 4 58.0223 nan 0.0100 0.6594
## 5 57.2902 nan 0.0100 0.7141
## 6 56.5739 nan 0.0100 0.7383
## 7 55.9285 nan 0.0100 0.6857
## 8 55.2359 nan 0.0100 0.6329
## 9 54.5971 nan 0.0100 0.6439
## 10 53.9664 nan 0.0100 0.6208
## 20 48.0228 nan 0.0100 0.5131
## 40 38.9565 nan 0.0100 0.3685
## 60 32.0781 nan 0.0100 0.2316
## 80 26.9190 nan 0.0100 0.1754
## 100 22.8628 nan 0.0100 0.1511
## 120 19.6574 nan 0.0100 0.1157
## 140 17.1367 nan 0.0100 0.1068
## 160 15.0963 nan 0.0100 0.0742
## 180 13.3989 nan 0.0100 0.0511
## 200 12.0526 nan 0.0100 0.0478
## 220 10.9251 nan 0.0100 0.0443
## 240 9.9798 nan 0.0100 0.0297
## 260 9.1773 nan 0.0100 0.0267
## 280 8.4882 nan 0.0100 0.0215
## 300 7.9452 nan 0.0100 0.0179
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.3358 nan 0.0100 0.7410
## 2 59.6381 nan 0.0100 0.7717
## 3 58.9181 nan 0.0100 0.7194
## 4 58.1778 nan 0.0100 0.7511
## 5 57.4200 nan 0.0100 0.6840
## 6 56.7205 nan 0.0100 0.6435
## 7 56.0325 nan 0.0100 0.6156
## 8 55.2890 nan 0.0100 0.6614
## 9 54.6191 nan 0.0100 0.5893
## 10 53.9630 nan 0.0100 0.6470
## 20 48.1116 nan 0.0100 0.5407
## 40 38.8595 nan 0.0100 0.3931
## 60 31.8734 nan 0.0100 0.2925
## 80 26.6940 nan 0.0100 0.2177
## 100 22.7128 nan 0.0100 0.1504
## 120 19.6294 nan 0.0100 0.1181
## 140 17.1552 nan 0.0100 0.1045
## 160 15.1093 nan 0.0100 0.0684
## 180 13.4372 nan 0.0100 0.0561
## 200 12.1154 nan 0.0100 0.0471
## 220 11.0314 nan 0.0100 0.0306
## 240 10.0815 nan 0.0100 0.0335
## 260 9.2700 nan 0.0100 0.0212
## 280 8.5830 nan 0.0100 0.0259
## 300 8.0039 nan 0.0100 0.0283
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.3345 nan 0.0100 0.7580
## 2 59.5236 nan 0.0100 0.7380
## 3 58.8020 nan 0.0100 0.7468
## 4 58.0023 nan 0.0100 0.7469
## 5 57.2910 nan 0.0100 0.7150
## 6 56.5618 nan 0.0100 0.6706
## 7 55.8565 nan 0.0100 0.6944
## 8 55.1813 nan 0.0100 0.6619
## 9 54.5096 nan 0.0100 0.6846
## 10 53.8259 nan 0.0100 0.6196
## 20 47.9957 nan 0.0100 0.4979
## 40 39.0757 nan 0.0100 0.3924
## 60 32.0510 nan 0.0100 0.2917
## 80 26.9588 nan 0.0100 0.2144
## 100 22.8597 nan 0.0100 0.1579
## 120 19.6858 nan 0.0100 0.1303
## 140 17.1854 nan 0.0100 0.0814
## 160 15.1114 nan 0.0100 0.0900
## 180 13.4924 nan 0.0100 0.0717
## 200 12.1109 nan 0.0100 0.0486
## 220 10.9870 nan 0.0100 0.0187
## 240 10.0550 nan 0.0100 0.0327
## 260 9.2751 nan 0.0100 0.0275
## 280 8.6431 nan 0.0100 0.0217
## 300 8.0886 nan 0.0100 0.0120
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.0433 nan 0.0100 0.9634
## 2 59.1019 nan 0.0100 0.9004
## 3 58.0928 nan 0.0100 0.8345
## 4 57.1744 nan 0.0100 0.8803
## 5 56.1992 nan 0.0100 1.0349
## 6 55.3106 nan 0.0100 0.9595
## 7 54.4148 nan 0.0100 0.9659
## 8 53.5565 nan 0.0100 0.8706
## 9 52.7349 nan 0.0100 0.8061
## 10 51.9506 nan 0.0100 0.7888
## 20 44.4523 nan 0.0100 0.7300
## 40 32.8263 nan 0.0100 0.4898
## 60 24.7771 nan 0.0100 0.3550
## 80 19.1013 nan 0.0100 0.1695
## 100 15.1295 nan 0.0100 0.1507
## 120 12.2295 nan 0.0100 0.1122
## 140 10.1323 nan 0.0100 0.0880
## 160 8.6122 nan 0.0100 0.0665
## 180 7.4402 nan 0.0100 0.0389
## 200 6.5393 nan 0.0100 0.0328
## 220 5.8724 nan 0.0100 0.0198
## 240 5.3834 nan 0.0100 0.0175
## 260 4.9905 nan 0.0100 0.0093
## 280 4.6939 nan 0.0100 0.0063
## 300 4.4521 nan 0.0100 -0.0038
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.0225 nan 0.0100 1.0045
## 2 58.9893 nan 0.0100 1.1062
## 3 58.0600 nan 0.0100 0.8951
## 4 57.1438 nan 0.0100 0.9146
## 5 56.1944 nan 0.0100 0.8960
## 6 55.3194 nan 0.0100 0.9240
## 7 54.4099 nan 0.0100 0.9043
## 8 53.4969 nan 0.0100 0.9374
## 9 52.5711 nan 0.0100 0.9185
## 10 51.7597 nan 0.0100 0.8255
## 20 44.2439 nan 0.0100 0.6944
## 40 32.7609 nan 0.0100 0.4770
## 60 24.6358 nan 0.0100 0.3200
## 80 18.9947 nan 0.0100 0.2315
## 100 15.0428 nan 0.0100 0.1512
## 120 12.2678 nan 0.0100 0.1038
## 140 10.1625 nan 0.0100 0.0823
## 160 8.6497 nan 0.0100 0.0484
## 180 7.5598 nan 0.0100 0.0451
## 200 6.7065 nan 0.0100 0.0309
## 220 6.0656 nan 0.0100 0.0145
## 240 5.5642 nan 0.0100 0.0142
## 260 5.1777 nan 0.0100 0.0095
## 280 4.8908 nan 0.0100 0.0092
## 300 4.6551 nan 0.0100 0.0076
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.0794 nan 0.0100 0.9119
## 2 59.0456 nan 0.0100 0.8568
## 3 58.0929 nan 0.0100 0.8442
## 4 57.1425 nan 0.0100 0.8541
## 5 56.1999 nan 0.0100 0.9438
## 6 55.2876 nan 0.0100 0.8320
## 7 54.3969 nan 0.0100 0.7467
## 8 53.5732 nan 0.0100 0.8576
## 9 52.7422 nan 0.0100 0.8188
## 10 51.8967 nan 0.0100 0.8492
## 20 44.3652 nan 0.0100 0.6170
## 40 32.7914 nan 0.0100 0.4969
## 60 24.7314 nan 0.0100 0.2972
## 80 19.1481 nan 0.0100 0.2199
## 100 15.2114 nan 0.0100 0.1497
## 120 12.3881 nan 0.0100 0.1018
## 140 10.3871 nan 0.0100 0.0864
## 160 8.8829 nan 0.0100 0.0540
## 180 7.7649 nan 0.0100 0.0396
## 200 6.9283 nan 0.0100 0.0306
## 220 6.3078 nan 0.0100 0.0191
## 240 5.8241 nan 0.0100 0.0129
## 260 5.4501 nan 0.0100 0.0027
## 280 5.1501 nan 0.0100 0.0063
## 300 4.9434 nan 0.0100 -0.0010
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.0306 nan 0.0100 1.0784
## 2 59.0280 nan 0.0100 1.0730
## 3 57.9801 nan 0.0100 1.0094
## 4 56.9847 nan 0.0100 1.0551
## 5 56.0450 nan 0.0100 1.0124
## 6 55.0641 nan 0.0100 1.0696
## 7 54.1206 nan 0.0100 0.9663
## 8 53.2228 nan 0.0100 0.9031
## 9 52.2910 nan 0.0100 0.9739
## 10 51.4144 nan 0.0100 0.9420
## 20 43.3036 nan 0.0100 0.6559
## 40 31.2040 nan 0.0100 0.4875
## 60 22.8807 nan 0.0100 0.3367
## 80 17.2145 nan 0.0100 0.2408
## 100 13.3191 nan 0.0100 0.1538
## 120 10.5060 nan 0.0100 0.1223
## 140 8.5624 nan 0.0100 0.0757
## 160 7.1432 nan 0.0100 0.0341
## 180 6.1330 nan 0.0100 0.0330
## 200 5.3965 nan 0.0100 0.0304
## 220 4.8386 nan 0.0100 0.0045
## 240 4.4328 nan 0.0100 0.0084
## 260 4.1189 nan 0.0100 0.0065
## 280 3.8600 nan 0.0100 0.0061
## 300 3.6612 nan 0.0100 0.0019
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.0031 nan 0.0100 1.1033
## 2 58.9591 nan 0.0100 1.0304
## 3 57.9819 nan 0.0100 0.9649
## 4 56.9504 nan 0.0100 0.9749
## 5 56.0079 nan 0.0100 0.8591
## 6 55.0450 nan 0.0100 0.9702
## 7 54.1063 nan 0.0100 0.8210
## 8 53.2218 nan 0.0100 0.8920
## 9 52.3569 nan 0.0100 0.8488
## 10 51.4483 nan 0.0100 0.9247
## 20 43.4312 nan 0.0100 0.7435
## 40 31.3662 nan 0.0100 0.4730
## 60 23.2167 nan 0.0100 0.3407
## 80 17.4073 nan 0.0100 0.2523
## 100 13.4865 nan 0.0100 0.1455
## 120 10.7289 nan 0.0100 0.1234
## 140 8.7456 nan 0.0100 0.0729
## 160 7.3458 nan 0.0100 0.0321
## 180 6.3556 nan 0.0100 0.0391
## 200 5.6343 nan 0.0100 0.0225
## 220 5.0878 nan 0.0100 0.0140
## 240 4.6723 nan 0.0100 0.0090
## 260 4.3701 nan 0.0100 0.0021
## 280 4.1307 nan 0.0100 0.0032
## 300 3.9457 nan 0.0100 0.0017
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.0601 nan 0.0100 0.9100
## 2 59.0133 nan 0.0100 1.1054
## 3 57.9875 nan 0.0100 0.8578
## 4 57.0170 nan 0.0100 0.9104
## 5 56.1023 nan 0.0100 0.9718
## 6 55.1425 nan 0.0100 0.7635
## 7 54.2256 nan 0.0100 1.0057
## 8 53.3202 nan 0.0100 0.9799
## 9 52.3989 nan 0.0100 0.8590
## 10 51.5373 nan 0.0100 0.7083
## 20 43.6384 nan 0.0100 0.7752
## 40 31.6797 nan 0.0100 0.4688
## 60 23.5365 nan 0.0100 0.3472
## 80 17.8371 nan 0.0100 0.2231
## 100 13.8891 nan 0.0100 0.1541
## 120 11.0696 nan 0.0100 0.1152
## 140 9.0807 nan 0.0100 0.0720
## 160 7.6880 nan 0.0100 0.0510
## 180 6.6640 nan 0.0100 0.0385
## 200 5.9594 nan 0.0100 0.0264
## 220 5.4381 nan 0.0100 0.0192
## 240 5.0597 nan 0.0100 0.0094
## 260 4.7629 nan 0.0100 0.0097
## 280 4.5301 nan 0.0100 0.0022
## 300 4.3435 nan 0.0100 0.0054
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.0686 nan 0.0500 3.9884
## 2 53.6808 nan 0.0500 3.2459
## 3 50.5747 nan 0.0500 3.0759
## 4 47.9277 nan 0.0500 2.7992
## 5 45.2158 nan 0.0500 2.5056
## 6 42.6245 nan 0.0500 2.3621
## 7 40.3680 nan 0.0500 2.0140
## 8 38.4157 nan 0.0500 1.9442
## 9 36.4579 nan 0.0500 1.6386
## 10 34.5504 nan 0.0500 1.8207
## 20 21.9010 nan 0.0500 0.6870
## 40 11.8403 nan 0.0500 0.2771
## 60 7.9130 nan 0.0500 0.1087
## 80 6.1085 nan 0.0500 0.0383
## 100 5.2288 nan 0.0500 0.0210
## 120 4.8862 nan 0.0500 -0.0014
## 140 4.6371 nan 0.0500 -0.0030
## 160 4.4916 nan 0.0500 -0.0114
## 180 4.4054 nan 0.0500 -0.0071
## 200 4.3184 nan 0.0500 -0.0503
## 220 4.2497 nan 0.0500 -0.0082
## 240 4.1732 nan 0.0500 -0.0034
## 260 4.1137 nan 0.0500 -0.0073
## 280 4.0693 nan 0.0500 -0.0171
## 300 4.0048 nan 0.0500 -0.0129
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.3032 nan 0.0500 3.4137
## 2 53.8451 nan 0.0500 3.3644
## 3 50.8419 nan 0.0500 3.0635
## 4 48.1059 nan 0.0500 2.8637
## 5 45.4951 nan 0.0500 2.6218
## 6 43.2009 nan 0.0500 2.2962
## 7 41.0027 nan 0.0500 2.1720
## 8 38.9278 nan 0.0500 2.3258
## 9 37.0991 nan 0.0500 1.1583
## 10 35.2996 nan 0.0500 1.7821
## 20 22.7003 nan 0.0500 0.7887
## 40 11.8425 nan 0.0500 0.2335
## 60 7.7871 nan 0.0500 0.0997
## 80 6.0970 nan 0.0500 0.0128
## 100 5.2797 nan 0.0500 0.0058
## 120 4.9291 nan 0.0500 0.0113
## 140 4.7586 nan 0.0500 0.0053
## 160 4.6136 nan 0.0500 0.0043
## 180 4.5174 nan 0.0500 -0.0162
## 200 4.4309 nan 0.0500 -0.0119
## 220 4.3630 nan 0.0500 -0.0095
## 240 4.3077 nan 0.0500 -0.0080
## 260 4.2591 nan 0.0500 -0.0239
## 280 4.2061 nan 0.0500 -0.0121
## 300 4.1674 nan 0.0500 -0.0157
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.0147 nan 0.0500 3.6156
## 2 53.6643 nan 0.0500 3.2812
## 3 50.5825 nan 0.0500 3.3877
## 4 47.7219 nan 0.0500 2.8477
## 5 44.9022 nan 0.0500 2.2341
## 6 42.7963 nan 0.0500 1.9367
## 7 40.2805 nan 0.0500 2.1741
## 8 38.2368 nan 0.0500 1.8505
## 9 36.2669 nan 0.0500 1.8008
## 10 34.6615 nan 0.0500 1.6843
## 20 22.1453 nan 0.0500 0.9306
## 40 11.9112 nan 0.0500 0.2382
## 60 8.0019 nan 0.0500 0.1019
## 80 6.2892 nan 0.0500 0.0461
## 100 5.4175 nan 0.0500 0.0038
## 120 5.0576 nan 0.0500 -0.0143
## 140 4.8511 nan 0.0500 -0.0234
## 160 4.7268 nan 0.0500 -0.0096
## 180 4.6364 nan 0.0500 -0.0134
## 200 4.5401 nan 0.0500 -0.0005
## 220 4.4543 nan 0.0500 -0.0058
## 240 4.3968 nan 0.0500 -0.0017
## 260 4.3379 nan 0.0500 -0.0115
## 280 4.2758 nan 0.0500 -0.0209
## 300 4.2173 nan 0.0500 -0.0118
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.9983 nan 0.0500 4.7744
## 2 51.3313 nan 0.0500 4.1629
## 3 47.2558 nan 0.0500 3.5812
## 4 43.5151 nan 0.0500 3.8992
## 5 40.3991 nan 0.0500 3.1273
## 6 37.2898 nan 0.0500 3.1562
## 7 34.5723 nan 0.0500 2.4273
## 8 32.0515 nan 0.0500 2.3127
## 9 29.7021 nan 0.0500 2.3771
## 10 27.6310 nan 0.0500 2.0703
## 20 14.6352 nan 0.0500 0.9613
## 40 6.4259 nan 0.0500 0.1341
## 60 4.3818 nan 0.0500 0.0446
## 80 3.7663 nan 0.0500 -0.0070
## 100 3.4074 nan 0.0500 -0.0090
## 120 3.1325 nan 0.0500 -0.0145
## 140 2.9468 nan 0.0500 -0.0280
## 160 2.7760 nan 0.0500 -0.0238
## 180 2.6049 nan 0.0500 -0.0169
## 200 2.4491 nan 0.0500 -0.0011
## 220 2.3390 nan 0.0500 -0.0172
## 240 2.2102 nan 0.0500 -0.0116
## 260 2.0966 nan 0.0500 -0.0148
## 280 2.0150 nan 0.0500 -0.0185
## 300 1.9287 nan 0.0500 -0.0253
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.1514 nan 0.0500 5.2119
## 2 51.9451 nan 0.0500 4.2393
## 3 47.9775 nan 0.0500 4.0969
## 4 44.1486 nan 0.0500 3.7577
## 5 40.9451 nan 0.0500 3.0020
## 6 37.9026 nan 0.0500 2.8061
## 7 35.1760 nan 0.0500 2.3815
## 8 32.5125 nan 0.0500 2.5488
## 9 30.0636 nan 0.0500 2.3018
## 10 27.9132 nan 0.0500 2.0051
## 20 14.8562 nan 0.0500 0.8340
## 40 6.5387 nan 0.0500 0.1066
## 60 4.5509 nan 0.0500 0.0100
## 80 3.9787 nan 0.0500 -0.0175
## 100 3.6682 nan 0.0500 -0.0467
## 120 3.4519 nan 0.0500 -0.0461
## 140 3.2717 nan 0.0500 -0.0283
## 160 3.0827 nan 0.0500 -0.0097
## 180 2.9268 nan 0.0500 -0.0071
## 200 2.8430 nan 0.0500 -0.0125
## 220 2.7379 nan 0.0500 -0.0200
## 240 2.6309 nan 0.0500 -0.0050
## 260 2.5530 nan 0.0500 -0.0132
## 280 2.4837 nan 0.0500 -0.0016
## 300 2.3895 nan 0.0500 -0.0092
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.1639 nan 0.0500 4.8764
## 2 51.9149 nan 0.0500 4.3815
## 3 47.8528 nan 0.0500 3.8368
## 4 43.9098 nan 0.0500 4.2040
## 5 40.3562 nan 0.0500 3.4795
## 6 37.2656 nan 0.0500 2.9575
## 7 34.6563 nan 0.0500 2.8532
## 8 32.1822 nan 0.0500 2.5280
## 9 29.8921 nan 0.0500 2.2462
## 10 27.9332 nan 0.0500 2.0290
## 20 14.9445 nan 0.0500 0.7714
## 40 6.9265 nan 0.0500 0.1457
## 60 4.9536 nan 0.0500 0.0053
## 80 4.3923 nan 0.0500 -0.0079
## 100 4.0726 nan 0.0500 -0.0061
## 120 3.8507 nan 0.0500 -0.0256
## 140 3.6559 nan 0.0500 -0.0085
## 160 3.5282 nan 0.0500 -0.0292
## 180 3.4002 nan 0.0500 -0.0157
## 200 3.2852 nan 0.0500 -0.0093
## 220 3.1708 nan 0.0500 -0.0330
## 240 3.0818 nan 0.0500 -0.0126
## 260 2.9886 nan 0.0500 -0.0144
## 280 2.8937 nan 0.0500 -0.0188
## 300 2.7978 nan 0.0500 -0.0347
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.8288 nan 0.0500 4.9513
## 2 50.9527 nan 0.0500 4.3735
## 3 46.5176 nan 0.0500 4.4041
## 4 42.7446 nan 0.0500 3.2939
## 5 39.3044 nan 0.0500 3.2678
## 6 36.3042 nan 0.0500 3.0930
## 7 33.4629 nan 0.0500 3.0656
## 8 31.0432 nan 0.0500 2.3087
## 9 28.7961 nan 0.0500 2.3715
## 10 26.8025 nan 0.0500 2.2347
## 20 13.1550 nan 0.0500 0.9706
## 40 5.3738 nan 0.0500 0.0882
## 60 3.7584 nan 0.0500 0.0220
## 80 3.0958 nan 0.0500 -0.0205
## 100 2.7100 nan 0.0500 -0.0301
## 120 2.4000 nan 0.0500 -0.0071
## 140 2.1467 nan 0.0500 -0.0151
## 160 1.9476 nan 0.0500 -0.0081
## 180 1.7633 nan 0.0500 -0.0275
## 200 1.6450 nan 0.0500 -0.0154
## 220 1.5266 nan 0.0500 -0.0122
## 240 1.4177 nan 0.0500 -0.0093
## 260 1.3207 nan 0.0500 -0.0064
## 280 1.2275 nan 0.0500 -0.0080
## 300 1.1415 nan 0.0500 -0.0056
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.1531 nan 0.0500 5.0049
## 2 51.4255 nan 0.0500 4.6784
## 3 47.0509 nan 0.0500 4.1250
## 4 43.2279 nan 0.0500 4.0666
## 5 39.8600 nan 0.0500 3.4924
## 6 36.4985 nan 0.0500 3.2027
## 7 33.6636 nan 0.0500 3.1167
## 8 30.9767 nan 0.0500 2.3953
## 9 28.6653 nan 0.0500 2.0766
## 10 26.4360 nan 0.0500 2.1462
## 20 13.2797 nan 0.0500 0.7163
## 40 5.5330 nan 0.0500 0.0784
## 60 3.9457 nan 0.0500 -0.0058
## 80 3.3950 nan 0.0500 0.0058
## 100 3.0695 nan 0.0500 -0.0232
## 120 2.8488 nan 0.0500 -0.0168
## 140 2.6675 nan 0.0500 -0.0203
## 160 2.4962 nan 0.0500 -0.0142
## 180 2.3408 nan 0.0500 -0.0338
## 200 2.2129 nan 0.0500 -0.0150
## 220 2.0818 nan 0.0500 -0.0183
## 240 1.9452 nan 0.0500 -0.0162
## 260 1.8487 nan 0.0500 -0.0238
## 280 1.7450 nan 0.0500 -0.0091
## 300 1.6702 nan 0.0500 -0.0106
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.7093 nan 0.0500 4.3436
## 2 51.0978 nan 0.0500 4.5788
## 3 46.9783 nan 0.0500 3.5963
## 4 43.1662 nan 0.0500 3.5175
## 5 39.5754 nan 0.0500 3.7428
## 6 36.6279 nan 0.0500 3.1279
## 7 33.8223 nan 0.0500 2.7487
## 8 31.1996 nan 0.0500 2.7334
## 9 28.6260 nan 0.0500 2.3199
## 10 26.5668 nan 0.0500 1.9730
## 20 13.3661 nan 0.0500 0.8031
## 40 5.8876 nan 0.0500 0.1000
## 60 4.3764 nan 0.0500 0.0065
## 80 3.8676 nan 0.0500 -0.0103
## 100 3.6180 nan 0.0500 -0.0241
## 120 3.3918 nan 0.0500 -0.0208
## 140 3.1635 nan 0.0500 -0.0167
## 160 2.9961 nan 0.0500 -0.0108
## 180 2.8291 nan 0.0500 -0.0169
## 200 2.6893 nan 0.0500 -0.0197
## 220 2.5717 nan 0.0500 -0.0157
## 240 2.4800 nan 0.0500 -0.0203
## 260 2.3711 nan 0.0500 -0.0177
## 280 2.2768 nan 0.0500 -0.0084
## 300 2.1801 nan 0.0500 -0.0170
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.8677 nan 0.1000 7.6444
## 2 47.8801 nan 0.1000 5.9888
## 3 42.7878 nan 0.1000 5.1558
## 4 38.5760 nan 0.1000 4.3065
## 5 34.6497 nan 0.1000 3.5810
## 6 31.1730 nan 0.1000 3.1862
## 7 28.3652 nan 0.1000 2.7487
## 8 25.9222 nan 0.1000 2.6672
## 9 24.0243 nan 0.1000 1.7219
## 10 22.1442 nan 0.1000 1.9251
## 20 11.8167 nan 0.1000 0.4500
## 40 6.0379 nan 0.1000 0.0177
## 60 4.8325 nan 0.1000 -0.0742
## 80 4.4516 nan 0.1000 -0.0102
## 100 4.2707 nan 0.1000 -0.0181
## 120 4.1316 nan 0.1000 -0.0186
## 140 4.0287 nan 0.1000 -0.0507
## 160 3.9035 nan 0.1000 -0.0315
## 180 3.7980 nan 0.1000 -0.0296
## 200 3.7133 nan 0.1000 -0.0530
## 220 3.6525 nan 0.1000 -0.0258
## 240 3.5851 nan 0.1000 -0.0149
## 260 3.5221 nan 0.1000 -0.0199
## 280 3.4688 nan 0.1000 -0.0119
## 300 3.3949 nan 0.1000 -0.0398
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.5084 nan 0.1000 7.1754
## 2 47.2144 nan 0.1000 6.3245
## 3 43.0341 nan 0.1000 3.7974
## 4 38.7386 nan 0.1000 4.5051
## 5 34.9836 nan 0.1000 3.8099
## 6 31.5748 nan 0.1000 3.1828
## 7 29.0431 nan 0.1000 2.8182
## 8 26.6336 nan 0.1000 2.0474
## 9 24.3920 nan 0.1000 1.8482
## 10 22.3207 nan 0.1000 1.8764
## 20 11.7955 nan 0.1000 0.4241
## 40 6.0267 nan 0.1000 0.0558
## 60 4.9952 nan 0.1000 0.0030
## 80 4.7756 nan 0.1000 -0.0193
## 100 4.5658 nan 0.1000 -0.0239
## 120 4.4196 nan 0.1000 -0.0105
## 140 4.2895 nan 0.1000 -0.0256
## 160 4.2042 nan 0.1000 -0.0218
## 180 4.1279 nan 0.1000 -0.0395
## 200 4.0555 nan 0.1000 -0.0101
## 220 3.9550 nan 0.1000 -0.0309
## 240 3.9001 nan 0.1000 -0.0213
## 260 3.8208 nan 0.1000 -0.0157
## 280 3.7665 nan 0.1000 -0.0222
## 300 3.7020 nan 0.1000 -0.0197
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.0329 nan 0.1000 6.6900
## 2 47.0447 nan 0.1000 5.9615
## 3 41.9518 nan 0.1000 4.8319
## 4 37.8651 nan 0.1000 3.5815
## 5 33.9618 nan 0.1000 3.5096
## 6 31.0610 nan 0.1000 2.9142
## 7 28.3769 nan 0.1000 2.2712
## 8 26.1858 nan 0.1000 1.7849
## 9 23.8608 nan 0.1000 1.9066
## 10 21.9904 nan 0.1000 1.6938
## 20 12.0102 nan 0.1000 0.4134
## 40 6.4074 nan 0.1000 0.0925
## 60 5.3255 nan 0.1000 0.0227
## 80 4.9871 nan 0.1000 -0.0068
## 100 4.7646 nan 0.1000 -0.0191
## 120 4.6076 nan 0.1000 -0.0103
## 140 4.4954 nan 0.1000 -0.0474
## 160 4.4027 nan 0.1000 -0.0107
## 180 4.3389 nan 0.1000 -0.0316
## 200 4.2413 nan 0.1000 -0.0377
## 220 4.1636 nan 0.1000 -0.0502
## 240 4.0818 nan 0.1000 -0.0055
## 260 4.0279 nan 0.1000 -0.0180
## 280 3.9776 nan 0.1000 -0.0232
## 300 3.9315 nan 0.1000 -0.0179
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.6074 nan 0.1000 9.4813
## 2 43.7770 nan 0.1000 7.1965
## 3 37.2460 nan 0.1000 6.6488
## 4 32.2083 nan 0.1000 4.7082
## 5 27.8199 nan 0.1000 3.7118
## 6 24.2310 nan 0.1000 3.6556
## 7 21.0037 nan 0.1000 2.6350
## 8 18.3308 nan 0.1000 2.4150
## 9 16.3724 nan 0.1000 1.6787
## 10 14.4304 nan 0.1000 1.7090
## 20 6.3774 nan 0.1000 0.3449
## 40 3.7627 nan 0.1000 -0.0162
## 60 3.1631 nan 0.1000 -0.0807
## 80 2.7504 nan 0.1000 -0.0546
## 100 2.4414 nan 0.1000 -0.0236
## 120 2.2242 nan 0.1000 -0.0403
## 140 2.0076 nan 0.1000 -0.0287
## 160 1.8313 nan 0.1000 -0.0143
## 180 1.6903 nan 0.1000 -0.0091
## 200 1.5727 nan 0.1000 -0.0307
## 220 1.4740 nan 0.1000 -0.0273
## 240 1.3888 nan 0.1000 -0.0209
## 260 1.3076 nan 0.1000 -0.0210
## 280 1.2323 nan 0.1000 -0.0126
## 300 1.1621 nan 0.1000 -0.0230
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.5395 nan 0.1000 8.8021
## 2 43.8727 nan 0.1000 7.0956
## 3 37.4693 nan 0.1000 6.4202
## 4 32.2181 nan 0.1000 3.9319
## 5 27.8648 nan 0.1000 4.1166
## 6 23.8674 nan 0.1000 3.5381
## 7 20.7736 nan 0.1000 2.7611
## 8 18.0459 nan 0.1000 2.5673
## 9 15.9883 nan 0.1000 2.1072
## 10 14.3939 nan 0.1000 1.4549
## 20 6.4929 nan 0.1000 0.2404
## 40 4.0851 nan 0.1000 -0.0125
## 60 3.5865 nan 0.1000 -0.0031
## 80 3.2412 nan 0.1000 -0.0388
## 100 2.9754 nan 0.1000 -0.0212
## 120 2.7093 nan 0.1000 -0.0204
## 140 2.5365 nan 0.1000 -0.0146
## 160 2.3725 nan 0.1000 -0.0258
## 180 2.2235 nan 0.1000 -0.0434
## 200 2.1015 nan 0.1000 -0.0280
## 220 1.9917 nan 0.1000 -0.0262
## 240 1.8868 nan 0.1000 -0.0275
## 260 1.7774 nan 0.1000 -0.0284
## 280 1.6900 nan 0.1000 -0.0134
## 300 1.6035 nan 0.1000 -0.0230
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.0311 nan 0.1000 9.5023
## 2 44.4135 nan 0.1000 7.6719
## 3 38.3621 nan 0.1000 6.1439
## 4 33.0221 nan 0.1000 5.6061
## 5 28.9102 nan 0.1000 4.2926
## 6 24.8364 nan 0.1000 4.2160
## 7 21.6394 nan 0.1000 3.0735
## 8 19.2988 nan 0.1000 2.5517
## 9 17.1599 nan 0.1000 2.1506
## 10 15.3016 nan 0.1000 1.5437
## 20 6.8800 nan 0.1000 0.2779
## 40 4.3824 nan 0.1000 0.0188
## 60 3.7977 nan 0.1000 -0.0475
## 80 3.4524 nan 0.1000 -0.0375
## 100 3.2551 nan 0.1000 -0.0109
## 120 3.0557 nan 0.1000 -0.0303
## 140 2.8644 nan 0.1000 -0.0209
## 160 2.6966 nan 0.1000 -0.0299
## 180 2.5677 nan 0.1000 -0.0218
## 200 2.4332 nan 0.1000 -0.0197
## 220 2.3221 nan 0.1000 -0.0402
## 240 2.2108 nan 0.1000 -0.0508
## 260 2.1140 nan 0.1000 -0.0159
## 280 2.0249 nan 0.1000 -0.0160
## 300 1.9395 nan 0.1000 -0.0182
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.9306 nan 0.1000 10.1220
## 2 42.4697 nan 0.1000 7.8818
## 3 35.5855 nan 0.1000 6.9267
## 4 30.2626 nan 0.1000 4.8325
## 5 25.4988 nan 0.1000 3.9239
## 6 21.7846 nan 0.1000 3.7323
## 7 19.1754 nan 0.1000 2.8123
## 8 16.5570 nan 0.1000 2.3310
## 9 14.4382 nan 0.1000 2.0743
## 10 12.6717 nan 0.1000 1.6600
## 20 5.2304 nan 0.1000 0.2012
## 40 3.1735 nan 0.1000 0.0042
## 60 2.4921 nan 0.1000 -0.0195
## 80 2.0801 nan 0.1000 -0.0476
## 100 1.7569 nan 0.1000 -0.0170
## 120 1.5026 nan 0.1000 -0.0407
## 140 1.3099 nan 0.1000 -0.0236
## 160 1.1569 nan 0.1000 -0.0164
## 180 1.0250 nan 0.1000 -0.0138
## 200 0.9268 nan 0.1000 -0.0218
## 220 0.8406 nan 0.1000 -0.0189
## 240 0.7582 nan 0.1000 -0.0075
## 260 0.6847 nan 0.1000 -0.0115
## 280 0.6190 nan 0.1000 -0.0160
## 300 0.5659 nan 0.1000 -0.0145
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.7677 nan 0.1000 10.3466
## 2 42.4192 nan 0.1000 7.7894
## 3 35.4452 nan 0.1000 6.0542
## 4 30.0178 nan 0.1000 4.9837
## 5 25.8880 nan 0.1000 3.6263
## 6 22.3799 nan 0.1000 3.0674
## 7 19.0905 nan 0.1000 3.0272
## 8 16.4027 nan 0.1000 2.4685
## 9 14.3910 nan 0.1000 2.2374
## 10 12.7889 nan 0.1000 1.6632
## 20 5.5572 nan 0.1000 0.0503
## 40 3.5114 nan 0.1000 -0.0139
## 60 2.9355 nan 0.1000 -0.0290
## 80 2.5536 nan 0.1000 -0.0541
## 100 2.3081 nan 0.1000 -0.0475
## 120 2.0764 nan 0.1000 -0.0234
## 140 1.9032 nan 0.1000 -0.0274
## 160 1.7320 nan 0.1000 -0.0303
## 180 1.5830 nan 0.1000 -0.0210
## 200 1.4703 nan 0.1000 -0.0157
## 220 1.3467 nan 0.1000 -0.0215
## 240 1.2332 nan 0.1000 -0.0204
## 260 1.1301 nan 0.1000 -0.0344
## 280 1.0400 nan 0.1000 -0.0146
## 300 0.9682 nan 0.1000 -0.0131
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.1003 nan 0.1000 9.3546
## 2 42.9178 nan 0.1000 7.4694
## 3 36.3539 nan 0.1000 6.5993
## 4 31.0834 nan 0.1000 5.2886
## 5 26.5855 nan 0.1000 4.1206
## 6 22.5874 nan 0.1000 3.6964
## 7 19.4913 nan 0.1000 3.1826
## 8 16.9955 nan 0.1000 2.6055
## 9 14.9757 nan 0.1000 1.9573
## 10 13.3151 nan 0.1000 1.5012
## 20 5.9095 nan 0.1000 0.2224
## 40 3.9130 nan 0.1000 -0.0003
## 60 3.3758 nan 0.1000 -0.0229
## 80 3.0844 nan 0.1000 -0.0496
## 100 2.8037 nan 0.1000 -0.0346
## 120 2.5693 nan 0.1000 -0.0050
## 140 2.3889 nan 0.1000 -0.0174
## 160 2.2223 nan 0.1000 -0.0345
## 180 2.0664 nan 0.1000 -0.0389
## 200 1.9129 nan 0.1000 -0.0247
## 220 1.7844 nan 0.1000 -0.0349
## 240 1.6862 nan 0.1000 -0.0264
## 260 1.5918 nan 0.1000 -0.0147
## 280 1.4980 nan 0.1000 -0.0341
## 300 1.3987 nan 0.1000 -0.0158
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.3660 nan 0.0500 4.8121
## 2 51.4808 nan 0.0500 4.4977
## 3 47.0687 nan 0.0500 4.8036
## 4 43.1309 nan 0.0500 3.9498
## 5 39.6542 nan 0.0500 3.3338
## 6 36.6243 nan 0.0500 2.9456
## 7 33.6848 nan 0.0500 2.8345
## 8 31.1140 nan 0.0500 2.5885
## 9 28.6350 nan 0.0500 2.2721
## 10 26.4003 nan 0.0500 1.9125
## 20 12.8657 nan 0.0500 0.7933
## 40 5.2493 nan 0.0500 0.1350
## 60 3.5775 nan 0.0500 -0.0106
## 80 2.9925 nan 0.0500 -0.0117
## 100 2.6640 nan 0.0500 -0.0236
## 120 2.4214 nan 0.0500 -0.0268
## 140 2.1867 nan 0.0500 -0.0006
## 160 1.9970 nan 0.0500 -0.0212
## 180 1.8204 nan 0.0500 -0.0191
## 200 1.6800 nan 0.0500 -0.0040
## 220 1.5605 nan 0.0500 -0.0062
## 240 1.4368 nan 0.0500 -0.0133
## 260 1.3442 nan 0.0500 -0.0080
## 280 1.2618 nan 0.0500 -0.0061
## 300 1.1867 nan 0.0500 -0.0132
##################################
# Reporting the cross-validation results
# for the GBM model
##################################
GBM_Tune## Stochastic Gradient Boosting
##
## 294 samples
## 5 predictor
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 266, 264, 265, 264, 265, 266, ...
## Resampling results across tuning parameters:
##
## shrinkage interaction.depth n.minobsinnode n.trees RMSE Rsquared
## 0.01 1 5 100 4.810935 0.8220024
## 0.01 1 5 200 3.591342 0.8649364
## 0.01 1 5 300 2.996879 0.8894673
## 0.01 1 10 100 4.804888 0.8245577
## 0.01 1 10 200 3.594237 0.8642448
## 0.01 1 10 300 2.996239 0.8897373
## 0.01 1 15 100 4.801958 0.8224158
## 0.01 1 15 200 3.580953 0.8641242
## 0.01 1 15 300 2.999924 0.8879375
## 0.01 3 5 100 3.973208 0.8904896
## 0.01 3 5 200 2.764060 0.9112249
## 0.01 3 5 300 2.425116 0.9185948
## 0.01 3 10 100 3.970773 0.8893448
## 0.01 3 10 200 2.771515 0.9103313
## 0.01 3 10 300 2.441187 0.9176932
## 0.01 3 15 100 3.990870 0.8850612
## 0.01 3 15 200 2.824472 0.9047488
## 0.01 3 15 300 2.489460 0.9135700
## 0.01 5 5 100 3.765540 0.9072375
## 0.01 5 5 200 2.618322 0.9166507
## 0.01 5 5 300 2.380487 0.9195383
## 0.01 5 10 100 3.761667 0.9075920
## 0.01 5 10 200 2.610134 0.9173829
## 0.01 5 10 300 2.374509 0.9205702
## 0.01 5 15 100 3.788274 0.9029016
## 0.01 5 15 200 2.658002 0.9129307
## 0.01 5 15 300 2.418481 0.9169741
## 0.05 1 5 100 2.532506 0.9087920
## 0.05 1 5 200 2.439447 0.9148728
## 0.05 1 5 300 2.437366 0.9142320
## 0.05 1 10 100 2.503174 0.9128347
## 0.05 1 10 200 2.378897 0.9209685
## 0.05 1 10 300 2.370010 0.9210478
## 0.05 1 15 100 2.563312 0.9068743
## 0.05 1 15 200 2.434734 0.9152549
## 0.05 1 15 300 2.438963 0.9157699
## 0.05 3 5 100 2.346351 0.9206420
## 0.05 3 5 200 2.345393 0.9199519
## 0.05 3 5 300 2.327349 0.9207466
## 0.05 3 10 100 2.360410 0.9194875
## 0.05 3 10 200 2.372344 0.9180728
## 0.05 3 10 300 2.358254 0.9191602
## 0.05 3 15 100 2.358306 0.9181427
## 0.05 3 15 200 2.357688 0.9187985
## 0.05 3 15 300 2.366932 0.9176364
## 0.05 5 5 100 2.344008 0.9188591
## 0.05 5 5 200 2.333080 0.9188954
## 0.05 5 5 300 2.319410 0.9198973
## 0.05 5 10 100 2.386873 0.9173964
## 0.05 5 10 200 2.385332 0.9171571
## 0.05 5 10 300 2.376674 0.9172431
## 0.05 5 15 100 2.374062 0.9175198
## 0.05 5 15 200 2.358935 0.9191052
## 0.05 5 15 300 2.360735 0.9193228
## 0.10 1 5 100 2.449071 0.9157931
## 0.10 1 5 200 2.452391 0.9146947
## 0.10 1 5 300 2.470079 0.9134424
## 0.10 1 10 100 2.465371 0.9133767
## 0.10 1 10 200 2.463359 0.9139698
## 0.10 1 10 300 2.445996 0.9142583
## 0.10 1 15 100 2.432597 0.9151404
## 0.10 1 15 200 2.455241 0.9150903
## 0.10 1 15 300 2.434320 0.9161904
## 0.10 3 5 100 2.352778 0.9177282
## 0.10 3 5 200 2.335391 0.9186142
## 0.10 3 5 300 2.335459 0.9178562
## 0.10 3 10 100 2.386385 0.9169080
## 0.10 3 10 200 2.348308 0.9189082
## 0.10 3 10 300 2.339177 0.9188534
## 0.10 3 15 100 2.363800 0.9195673
## 0.10 3 15 200 2.380596 0.9181203
## 0.10 3 15 300 2.362293 0.9199136
## 0.10 5 5 100 2.393913 0.9154127
## 0.10 5 5 200 2.391989 0.9153164
## 0.10 5 5 300 2.398330 0.9139535
## 0.10 5 10 100 2.376873 0.9169394
## 0.10 5 10 200 2.361392 0.9175143
## 0.10 5 10 300 2.392849 0.9160077
## 0.10 5 15 100 2.429391 0.9143777
## 0.10 5 15 200 2.434540 0.9141983
## 0.10 5 15 300 2.452960 0.9136757
## MAE
## 3.877301
## 2.888462
## 2.363814
## 3.869919
## 2.880853
## 2.347499
## 3.863080
## 2.868756
## 2.344566
## 3.273956
## 2.172907
## 1.849299
## 3.274361
## 2.175127
## 1.838932
## 3.278500
## 2.205304
## 1.878976
## 3.103002
## 2.055751
## 1.801944
## 3.090197
## 2.035921
## 1.768429
## 3.111034
## 2.050567
## 1.799572
## 1.905685
## 1.804011
## 1.782111
## 1.887731
## 1.766298
## 1.747344
## 1.960129
## 1.813449
## 1.805008
## 1.755183
## 1.746585
## 1.732468
## 1.738475
## 1.730635
## 1.716780
## 1.749207
## 1.731617
## 1.730280
## 1.725602
## 1.714908
## 1.716469
## 1.773769
## 1.756986
## 1.742463
## 1.768412
## 1.744998
## 1.746614
## 1.821177
## 1.802574
## 1.807528
## 1.842272
## 1.826997
## 1.800826
## 1.783575
## 1.795195
## 1.779606
## 1.738886
## 1.740522
## 1.747177
## 1.739180
## 1.719281
## 1.716109
## 1.760685
## 1.758236
## 1.747422
## 1.749146
## 1.762114
## 1.763512
## 1.746935
## 1.736254
## 1.767303
## 1.792245
## 1.787265
## 1.807362
##
## RMSE was used to select the optimal model using the smallest value.
## The final values used for the model were n.trees = 300, interaction.depth =
## 5, shrinkage = 0.05 and n.minobsinnode = 5.
GBM_Tune$finalModel## A gradient boosted model with gaussian loss function.
## 300 iterations were performed.
## There were 5 predictors of which 5 had non-zero influence.
(GBM_Tune_RMSE <- GBM_Tune$results[GBM_Tune$results$shrinkage==GBM_Tune$bestTune$shrinkage &
GBM_Tune$results$interaction.depth==GBM_Tune$bestTune$interaction.depth &
GBM_Tune$results$n.minobsinnode==GBM_Tune$bestTune$n.minobsinnode &
GBM_Tune$results$n.trees==GBM_Tune$bestTune$n.trees,
c("RMSE")])## [1] 2.31941
(GBM_Tune_Rsquared <- GBM_Tune$results[GBM_Tune$results$shrinkage==GBM_Tune$bestTune$shrinkage &
GBM_Tune$results$interaction.depth==GBM_Tune$bestTune$interaction.depth &
GBM_Tune$results$n.minobsinnode==GBM_Tune$bestTune$n.minobsinnode &
GBM_Tune$results$n.trees==GBM_Tune$bestTune$n.trees,
c("Rsquared")])## [1] 0.9198973
(GBM_Tune_MAE <- GBM_Tune$results[GBM_Tune$results$shrinkage==GBM_Tune$bestTune$shrinkage &
GBM_Tune$results$interaction.depth==GBM_Tune$bestTune$interaction.depth &
GBM_Tune$results$n.minobsinnode==GBM_Tune$bestTune$n.minobsinnode &
GBM_Tune$results$n.trees==GBM_Tune$bestTune$n.trees,
c("MAE")])## [1] 1.716469
##################################
# Identifying and plotting the
# best model predictors
# for the GBM model
##################################
GBM_VarImp <- varImp(GBM_Tune, scale = TRUE)
plot(GBM_VarImp,
scales=list(y=list(cex = .95)),
main="Ranked Variable Importance : GBM",
xlab="Scaled Variable Importance Metrics",
ylab="Predictors",
cex=2,
origin=0,
alpha=0.45)##################################
# Defining the model hyperparameter values
# for the RF model
##################################
RF_Grid = data.frame(mtry = c(100, 200, 300, 400, 500,
600, 700, 800, 900, 1000))
##################################
# Running the RF model
# by setting the caret method to 'RF'
##################################
set.seed(12345678)
RF_Tune <- train(x = MD.Model.Predictors,
y = MD$LIFEXP,
method = "rf",
tuneGrid = RF_Grid,
trControl = KFold_Control)
##################################
# Reporting the cross-validation results
# for the RF model
##################################
RF_Tune## Random Forest
##
## 294 samples
## 5 predictor
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 266, 264, 265, 264, 265, 266, ...
## Resampling results across tuning parameters:
##
## mtry RMSE Rsquared MAE
## 100 2.469134 0.9069495 1.804822
## 200 2.468106 0.9065615 1.798157
## 300 2.467568 0.9067371 1.800105
## 400 2.454183 0.9077840 1.785219
## 500 2.485343 0.9058410 1.814855
## 600 2.489623 0.9053762 1.817485
## 700 2.480923 0.9058562 1.809700
## 800 2.476728 0.9064718 1.806681
## 900 2.476405 0.9065263 1.801839
## 1000 2.492884 0.9053344 1.814941
##
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was mtry = 400.
RF_Tune$finalModel##
## Call:
## randomForest(x = x, y = y, mtry = param$mtry)
## Type of random forest: regression
## Number of trees: 500
## No. of variables tried at each split: 5
##
## Mean of squared residuals: 6.28444
## % Var explained: 89.77
(RF_Tune_RMSE <- RF_Tune$results[RF_Tune$results$mtry==RF_Tune$bestTune$mtry,
c("RMSE")])## [1] 2.454183
(RF_Tune_Rsquared <- RF_Tune$results[RF_Tune$results$mtry==RF_Tune$bestTune$mtry,
c("Rsquared")])## [1] 0.907784
(RF_Tune_MAE <- RF_Tune$results[RF_Tune$results$mtry==RF_Tune$bestTune$mtry,
c("MAE")])## [1] 1.785219
##################################
# Identifying and plotting the
# best model predictors
# for the RF model
##################################
RF_VarImp <- varImp(RF_Tune, scale = TRUE)
plot(RF_VarImp,
scales=list(y=list(cex = .95)),
main="Ranked Variable Importance : RF",
xlab="Scaled Variable Importance Metrics",
ylab="Predictors",
cex=2,
origin=0,
alpha=0.45)##################################
# Defining the model hyperparameter values
# for the NN model
##################################
NN_Grid = expand.grid(size = c(2, 5, 10, 15, 20),
decay = c(0, 0.1, 0.001, 0.0001, 0.00001))
##################################
# Running the NN model
# by setting the caret method to 'NN'
##################################
set.seed(12345678)
NN_Tune <- train(x = MD.Model.Predictors,
y = MD$LIFEXP,
method = "nnet",
linout = TRUE,
preProcess = c('center', 'scale'),
maxit = 500,
tuneGrid = NN_Grid,
trControl = KFold_Control)## # weights: 15
## initial value 1406972.747928
## iter 10 value 17306.731946
## final value 16734.547507
## converged
## # weights: 36
## initial value 1386115.389324
## iter 10 value 3747.659411
## iter 20 value 1974.438861
## iter 30 value 1480.043876
## iter 40 value 1203.077370
## iter 50 value 1099.257974
## iter 60 value 1060.936116
## iter 70 value 1040.946828
## iter 80 value 1034.781443
## iter 90 value 1026.625616
## iter 100 value 1006.773023
## iter 110 value 986.220318
## iter 120 value 957.503659
## iter 130 value 945.283708
## iter 140 value 934.378541
## iter 150 value 926.520491
## iter 160 value 924.816890
## iter 170 value 917.440515
## iter 180 value 914.467570
## iter 190 value 911.122985
## iter 200 value 909.759038
## iter 210 value 909.663170
## iter 220 value 909.372418
## iter 230 value 909.342318
## iter 240 value 908.941445
## iter 250 value 907.867688
## iter 260 value 907.334520
## iter 270 value 907.266447
## iter 280 value 907.253068
## iter 290 value 907.074096
## iter 300 value 906.765869
## final value 906.754491
## converged
## # weights: 71
## initial value 1353444.522438
## iter 10 value 1648.263520
## iter 20 value 1204.419231
## iter 30 value 1072.404973
## iter 40 value 996.768999
## iter 50 value 955.077615
## iter 60 value 926.091176
## iter 70 value 889.713680
## iter 80 value 835.505714
## iter 90 value 800.676805
## iter 100 value 777.747877
## iter 110 value 743.926912
## iter 120 value 705.465974
## iter 130 value 659.865568
## iter 140 value 625.813797
## iter 150 value 619.258581
## iter 160 value 616.155254
## iter 170 value 606.049414
## iter 180 value 595.699481
## iter 190 value 588.072845
## iter 200 value 580.056197
## iter 210 value 567.804995
## iter 220 value 562.505124
## iter 230 value 558.441302
## iter 240 value 553.771010
## iter 250 value 547.974068
## iter 260 value 542.919518
## iter 270 value 539.736711
## iter 280 value 535.595847
## iter 290 value 533.118204
## iter 300 value 532.333468
## iter 310 value 531.018371
## iter 320 value 529.518356
## iter 330 value 528.566091
## iter 340 value 527.798319
## iter 350 value 526.952978
## iter 360 value 525.626017
## iter 370 value 524.360393
## iter 380 value 521.255604
## iter 390 value 519.689711
## iter 400 value 518.712474
## iter 410 value 518.294930
## iter 420 value 518.007947
## iter 430 value 517.981217
## iter 440 value 517.980722
## iter 450 value 517.980321
## iter 460 value 517.979203
## iter 470 value 517.977310
## iter 480 value 517.968718
## iter 490 value 517.837477
## iter 500 value 517.662914
## final value 517.662914
## stopped after 500 iterations
## # weights: 106
## initial value 1409418.840789
## iter 10 value 1325.784047
## iter 20 value 1088.694235
## iter 30 value 962.449694
## iter 40 value 847.693836
## iter 50 value 782.145588
## iter 60 value 695.632632
## iter 70 value 627.861612
## iter 80 value 594.493835
## iter 90 value 575.059432
## iter 100 value 561.086218
## iter 110 value 534.329803
## iter 120 value 490.066511
## iter 130 value 465.480794
## iter 140 value 442.739698
## iter 150 value 431.119941
## iter 160 value 423.454706
## iter 170 value 415.513606
## iter 180 value 409.290801
## iter 190 value 404.673111
## iter 200 value 399.794798
## iter 210 value 396.095946
## iter 220 value 394.305084
## iter 230 value 393.367340
## iter 240 value 391.575001
## iter 250 value 388.956156
## iter 260 value 383.932899
## iter 270 value 372.170220
## iter 280 value 364.740221
## iter 290 value 359.509029
## iter 300 value 351.256116
## iter 310 value 341.675288
## iter 320 value 338.290422
## iter 330 value 336.223563
## iter 340 value 334.964544
## iter 350 value 334.068692
## iter 360 value 333.505675
## iter 370 value 332.808529
## iter 380 value 331.425240
## iter 390 value 329.613177
## iter 400 value 328.332686
## iter 410 value 327.775660
## iter 420 value 327.594785
## iter 430 value 327.531307
## iter 440 value 327.529235
## iter 450 value 327.522892
## iter 460 value 327.514081
## iter 470 value 327.500114
## iter 480 value 327.461898
## iter 490 value 327.224713
## iter 500 value 326.843868
## final value 326.843868
## stopped after 500 iterations
## # weights: 141
## initial value 1398973.066738
## iter 10 value 1686.016326
## iter 20 value 1127.060603
## iter 30 value 944.645340
## iter 40 value 822.849266
## iter 50 value 737.427430
## iter 60 value 671.350469
## iter 70 value 629.045609
## iter 80 value 594.349911
## iter 90 value 562.372525
## iter 100 value 515.929303
## iter 110 value 481.253627
## iter 120 value 442.570930
## iter 130 value 413.209126
## iter 140 value 389.418247
## iter 150 value 367.598205
## iter 160 value 352.650564
## iter 170 value 336.207834
## iter 180 value 323.775668
## iter 190 value 316.764159
## iter 200 value 310.562379
## iter 210 value 301.334935
## iter 220 value 294.104293
## iter 230 value 283.069245
## iter 240 value 273.404821
## iter 250 value 267.714611
## iter 260 value 262.482541
## iter 270 value 253.395413
## iter 280 value 247.847948
## iter 290 value 245.496879
## iter 300 value 244.307030
## iter 310 value 242.492200
## iter 320 value 240.667713
## iter 330 value 239.380813
## iter 340 value 237.968113
## iter 350 value 235.637956
## iter 360 value 229.843322
## iter 370 value 226.485616
## iter 380 value 222.461939
## iter 390 value 215.550406
## iter 400 value 211.480761
## iter 410 value 206.621390
## iter 420 value 201.630312
## iter 430 value 198.889249
## iter 440 value 195.740854
## iter 450 value 193.957590
## iter 460 value 192.173457
## iter 470 value 190.926555
## iter 480 value 189.716988
## iter 490 value 187.874512
## iter 500 value 186.725424
## final value 186.725424
## stopped after 500 iterations
## # weights: 15
## initial value 1400381.846014
## iter 10 value 7067.881683
## iter 20 value 6206.286589
## iter 30 value 3467.746758
## iter 40 value 2324.081266
## iter 50 value 2069.308343
## iter 60 value 1971.540882
## iter 70 value 1733.850963
## iter 80 value 1674.525653
## iter 90 value 1668.600766
## iter 100 value 1662.156476
## final value 1662.044228
## converged
## # weights: 36
## initial value 1425263.534021
## iter 10 value 19451.969836
## iter 20 value 9160.229645
## iter 30 value 7600.056376
## iter 40 value 6700.328755
## iter 50 value 4604.195084
## iter 60 value 3041.680951
## iter 70 value 2514.659972
## iter 80 value 2027.325192
## iter 90 value 1727.217162
## iter 100 value 1519.706835
## iter 110 value 1456.669525
## iter 120 value 1425.909883
## iter 130 value 1411.163155
## iter 140 value 1383.288110
## iter 150 value 1332.521492
## iter 160 value 1268.309496
## iter 170 value 1222.208689
## iter 180 value 1210.416153
## iter 190 value 1202.110126
## iter 200 value 1191.508554
## iter 210 value 1188.634218
## iter 220 value 1181.793186
## iter 230 value 1175.078267
## iter 240 value 1172.038537
## iter 250 value 1171.081216
## final value 1171.037905
## converged
## # weights: 71
## initial value 1384131.732456
## iter 10 value 1976.543920
## iter 20 value 1363.628476
## iter 30 value 1218.826059
## iter 40 value 1134.281800
## iter 50 value 1054.321612
## iter 60 value 1005.843954
## iter 70 value 969.627519
## iter 80 value 938.650414
## iter 90 value 921.522617
## iter 100 value 904.542103
## iter 110 value 894.117665
## iter 120 value 884.474109
## iter 130 value 878.770902
## iter 140 value 876.683479
## iter 150 value 872.973336
## iter 160 value 867.828572
## iter 170 value 863.333203
## iter 180 value 861.022376
## iter 190 value 859.544386
## iter 200 value 853.591475
## iter 210 value 850.086686
## iter 220 value 848.678059
## iter 230 value 846.452737
## iter 240 value 845.705613
## iter 250 value 845.518519
## iter 260 value 845.507494
## final value 845.507466
## converged
## # weights: 106
## initial value 1414503.210071
## iter 10 value 1343.433832
## iter 20 value 1146.624886
## iter 30 value 1023.869360
## iter 40 value 953.781776
## iter 50 value 886.929725
## iter 60 value 855.866975
## iter 70 value 828.138589
## iter 80 value 805.855024
## iter 90 value 791.941820
## iter 100 value 777.446265
## iter 110 value 764.281980
## iter 120 value 749.008650
## iter 130 value 736.414466
## iter 140 value 730.142448
## iter 150 value 723.423440
## iter 160 value 716.986154
## iter 170 value 711.967611
## iter 180 value 708.721393
## iter 190 value 701.550418
## iter 200 value 693.816749
## iter 210 value 688.987131
## iter 220 value 686.398192
## iter 230 value 684.483765
## iter 240 value 681.942192
## iter 250 value 680.032327
## iter 260 value 679.198732
## iter 270 value 678.903392
## iter 280 value 678.789425
## iter 290 value 678.684709
## iter 300 value 678.633211
## iter 310 value 678.624871
## iter 320 value 678.624009
## final value 678.623923
## converged
## # weights: 141
## initial value 1366761.165719
## iter 10 value 1673.371539
## iter 20 value 1286.191377
## iter 30 value 1155.802442
## iter 40 value 1068.274149
## iter 50 value 979.125330
## iter 60 value 923.191796
## iter 70 value 877.834055
## iter 80 value 825.655591
## iter 90 value 790.561121
## iter 100 value 763.789031
## iter 110 value 750.172786
## iter 120 value 735.780027
## iter 130 value 721.767504
## iter 140 value 709.501881
## iter 150 value 696.912583
## iter 160 value 681.545364
## iter 170 value 674.312137
## iter 180 value 670.400298
## iter 190 value 666.222116
## iter 200 value 661.160247
## iter 210 value 654.435844
## iter 220 value 647.226444
## iter 230 value 640.727026
## iter 240 value 636.886235
## iter 250 value 631.626632
## iter 260 value 624.508053
## iter 270 value 619.924054
## iter 280 value 617.545892
## iter 290 value 616.439462
## iter 300 value 614.714624
## iter 310 value 611.756548
## iter 320 value 608.852541
## iter 330 value 605.749551
## iter 340 value 601.551114
## iter 350 value 593.004595
## iter 360 value 586.882036
## iter 370 value 583.490620
## iter 380 value 581.778686
## iter 390 value 580.889623
## iter 400 value 580.289483
## iter 410 value 579.851242
## iter 420 value 579.543287
## iter 430 value 579.238153
## iter 440 value 578.992822
## iter 450 value 578.887321
## iter 460 value 578.874498
## iter 470 value 578.872196
## final value 578.871982
## converged
## # weights: 15
## initial value 1419096.180400
## iter 10 value 24243.164713
## iter 20 value 16818.767637
## iter 30 value 16783.728277
## iter 40 value 9259.528519
## iter 50 value 7749.686175
## iter 60 value 5718.652105
## iter 70 value 3114.259466
## iter 80 value 1818.432927
## iter 90 value 1786.490041
## iter 100 value 1774.681323
## iter 110 value 1767.391368
## iter 120 value 1764.334013
## iter 130 value 1761.385886
## final value 1761.294808
## converged
## # weights: 36
## initial value 1429100.960345
## iter 10 value 24465.631986
## iter 20 value 17605.222419
## iter 30 value 10533.261836
## iter 40 value 4307.695702
## iter 50 value 1662.748767
## iter 60 value 1247.991988
## iter 70 value 1147.915670
## iter 80 value 1125.887587
## iter 90 value 1114.808595
## iter 100 value 1104.140790
## iter 110 value 1087.812745
## iter 120 value 1072.520031
## iter 130 value 1052.024077
## iter 140 value 1040.765059
## iter 150 value 1030.494747
## iter 160 value 1028.937603
## iter 170 value 1021.338011
## iter 180 value 1013.486271
## iter 190 value 1004.193710
## iter 200 value 988.909341
## iter 210 value 973.125634
## iter 220 value 962.630823
## iter 230 value 962.094535
## iter 240 value 960.885653
## iter 250 value 959.483035
## iter 260 value 955.910934
## iter 270 value 954.679085
## iter 280 value 954.069128
## iter 290 value 953.736815
## iter 300 value 953.624310
## iter 310 value 953.482168
## iter 320 value 952.966266
## iter 330 value 952.093411
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## iter 350 value 950.911766
## iter 360 value 950.816826
## iter 370 value 950.769063
## iter 380 value 950.766590
## iter 390 value 950.751871
## iter 400 value 950.740473
## iter 410 value 950.727509
## iter 410 value 950.727508
## iter 410 value 950.727508
## final value 950.727508
## converged
## # weights: 71
## initial value 1401712.653461
## iter 10 value 1738.568841
## iter 20 value 1155.969694
## iter 30 value 1066.155055
## iter 40 value 994.421390
## iter 50 value 926.207042
## iter 60 value 885.460370
## iter 70 value 846.641929
## iter 80 value 785.768292
## iter 90 value 761.296563
## iter 100 value 745.013847
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## iter 120 value 726.662173
## iter 130 value 711.057169
## iter 140 value 696.898191
## iter 150 value 691.769244
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## iter 200 value 620.104902
## iter 210 value 606.666531
## iter 220 value 598.862752
## iter 230 value 593.971127
## iter 240 value 587.916606
## iter 250 value 585.268804
## iter 260 value 582.687583
## iter 270 value 581.012096
## iter 280 value 579.174268
## iter 290 value 576.781503
## iter 300 value 576.419454
## iter 310 value 575.270715
## iter 320 value 572.051387
## iter 330 value 570.105102
## iter 340 value 568.706047
## iter 350 value 562.401978
## iter 360 value 541.358945
## iter 370 value 537.930960
## iter 380 value 534.613389
## iter 390 value 533.733992
## iter 400 value 532.607633
## iter 410 value 532.221327
## iter 420 value 532.085708
## iter 430 value 530.919608
## iter 440 value 524.509179
## iter 450 value 519.374898
## iter 460 value 518.269591
## iter 470 value 517.068506
## iter 480 value 516.304350
## iter 490 value 516.230214
## iter 500 value 516.227264
## final value 516.227264
## stopped after 500 iterations
## # weights: 106
## initial value 1387542.547875
## iter 10 value 2235.721775
## iter 20 value 1219.681810
## iter 30 value 926.131440
## iter 40 value 765.195906
## iter 50 value 690.558284
## iter 60 value 634.275832
## iter 70 value 592.590454
## iter 80 value 547.540247
## iter 90 value 517.199684
## iter 100 value 493.515042
## iter 110 value 479.050776
## iter 120 value 470.329222
## iter 130 value 463.030730
## iter 140 value 449.715226
## iter 150 value 437.567532
## iter 160 value 432.195300
## iter 170 value 427.655493
## iter 180 value 425.090481
## iter 190 value 422.483890
## iter 200 value 419.598959
## iter 210 value 417.697275
## iter 220 value 416.829767
## iter 230 value 416.463387
## iter 240 value 416.068524
## iter 250 value 415.563033
## iter 260 value 414.463444
## iter 270 value 412.521118
## iter 280 value 410.971977
## iter 290 value 409.564001
## iter 300 value 408.638900
## iter 310 value 407.829637
## iter 320 value 407.066443
## iter 330 value 407.027775
## iter 340 value 407.015843
## iter 350 value 407.008033
## iter 360 value 407.006771
## iter 370 value 407.006464
## final value 407.006419
## converged
## # weights: 141
## initial value 1402239.465710
## iter 10 value 3053.206236
## iter 20 value 1196.290006
## iter 30 value 926.935640
## iter 40 value 767.047447
## iter 50 value 682.034414
## iter 60 value 627.714926
## iter 70 value 560.983523
## iter 80 value 508.656911
## iter 90 value 473.707737
## iter 100 value 446.345624
## iter 110 value 426.729946
## iter 120 value 411.078464
## iter 130 value 392.680100
## iter 140 value 370.224893
## iter 150 value 337.476501
## iter 160 value 307.837431
## iter 170 value 299.046501
## iter 180 value 295.389558
## iter 190 value 291.701766
## iter 200 value 285.363260
## iter 210 value 275.058940
## iter 220 value 262.991061
## iter 230 value 256.318150
## iter 240 value 252.021116
## iter 250 value 245.599478
## iter 260 value 243.138813
## iter 270 value 241.202795
## iter 280 value 239.584831
## iter 290 value 238.314803
## iter 300 value 237.212042
## iter 310 value 235.419781
## iter 320 value 233.273965
## iter 330 value 231.424120
## iter 340 value 228.110930
## iter 350 value 224.330065
## iter 360 value 220.771646
## iter 370 value 217.167106
## iter 380 value 212.917724
## iter 390 value 209.748084
## iter 400 value 208.313777
## iter 410 value 205.747497
## iter 420 value 203.215240
## iter 430 value 201.087043
## iter 440 value 200.084978
## iter 450 value 199.753660
## iter 460 value 199.119059
## iter 470 value 198.118019
## iter 480 value 197.129015
## iter 490 value 196.066459
## iter 500 value 195.341673
## final value 195.341673
## stopped after 500 iterations
## # weights: 15
## initial value 1412281.169006
## iter 10 value 6731.743776
## iter 20 value 6379.033364
## iter 30 value 5647.390901
## iter 40 value 5633.300355
## iter 50 value 5577.514875
## iter 60 value 5551.306084
## iter 70 value 4977.434185
## iter 80 value 2776.380282
## iter 90 value 1932.744927
## iter 100 value 1887.601266
## iter 110 value 1847.979838
## iter 120 value 1835.088293
## iter 130 value 1833.105497
## iter 140 value 1828.924685
## iter 150 value 1826.369446
## iter 160 value 1825.722543
## final value 1825.720895
## converged
## # weights: 36
## initial value 1390095.098143
## iter 10 value 5041.909548
## iter 20 value 2366.320459
## iter 30 value 1839.238992
## iter 40 value 1707.588729
## iter 50 value 1679.127363
## iter 60 value 1618.661903
## iter 70 value 1484.753328
## iter 80 value 1392.180254
## iter 90 value 1299.002627
## iter 100 value 1268.296648
## iter 110 value 1244.937003
## iter 120 value 1147.495392
## iter 130 value 1124.845607
## iter 140 value 1120.836930
## iter 150 value 1118.772810
## iter 160 value 1118.163975
## iter 170 value 1117.728992
## iter 180 value 1117.453079
## iter 190 value 1116.382995
## iter 200 value 1116.174988
## iter 210 value 1115.945301
## iter 220 value 1115.726556
## iter 230 value 1115.629768
## iter 240 value 1115.568923
## iter 250 value 1115.566601
## iter 260 value 1115.303561
## iter 270 value 1114.941393
## iter 280 value 1113.969025
## iter 290 value 1112.774834
## iter 300 value 1103.992224
## iter 310 value 1089.450516
## iter 320 value 1083.596173
## iter 330 value 1080.955721
## iter 340 value 1080.601806
## iter 350 value 1077.281110
## iter 360 value 1075.715071
## iter 370 value 1075.612399
## iter 380 value 1074.594064
## iter 390 value 1073.595974
## iter 400 value 1073.333542
## iter 410 value 1072.920142
## iter 420 value 1072.410112
## iter 430 value 1072.237846
## iter 440 value 1072.234172
## iter 450 value 1072.214131
## iter 460 value 1071.926730
## iter 470 value 1071.807091
## iter 480 value 1071.776645
## iter 490 value 1071.749801
## iter 500 value 1071.711436
## final value 1071.711436
## stopped after 500 iterations
## # weights: 71
## initial value 1410171.270412
## iter 10 value 3922.442191
## iter 20 value 1845.385299
## iter 30 value 1367.609950
## iter 40 value 1235.177604
## iter 50 value 1124.674640
## iter 60 value 1036.832182
## iter 70 value 965.602156
## iter 80 value 930.183095
## iter 90 value 903.169432
## iter 100 value 883.843379
## iter 110 value 868.816377
## iter 120 value 858.561122
## iter 130 value 841.498351
## iter 140 value 834.071478
## iter 150 value 832.656036
## iter 160 value 832.305317
## iter 170 value 829.076600
## iter 180 value 825.246079
## iter 190 value 814.529060
## iter 200 value 803.113719
## iter 210 value 793.630889
## iter 220 value 789.577656
## iter 230 value 786.744274
## iter 240 value 784.402387
## iter 250 value 782.373796
## iter 260 value 781.749209
## iter 270 value 781.124557
## iter 280 value 779.764384
## iter 290 value 775.887748
## iter 300 value 775.236538
## iter 310 value 774.331260
## iter 320 value 773.657741
## iter 330 value 773.232376
## iter 340 value 771.625118
## iter 350 value 769.012867
## iter 360 value 765.343257
## iter 370 value 764.266088
## iter 380 value 763.993307
## iter 390 value 763.806990
## iter 400 value 763.574420
## iter 410 value 763.354519
## iter 420 value 763.202166
## iter 430 value 763.118776
## iter 440 value 763.105596
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## iter 470 value 761.849135
## iter 480 value 758.911857
## iter 490 value 756.850933
## iter 500 value 756.137036
## final value 756.137036
## stopped after 500 iterations
## # weights: 106
## initial value 1478360.623413
## iter 10 value 1332.081732
## iter 20 value 1074.661955
## iter 30 value 948.322799
## iter 40 value 847.519714
## iter 50 value 790.092241
## iter 60 value 729.444990
## iter 70 value 663.083422
## iter 80 value 615.765612
## iter 90 value 588.464522
## iter 100 value 574.147784
## iter 110 value 550.918580
## iter 120 value 523.963058
## iter 130 value 500.123274
## iter 140 value 484.232037
## iter 150 value 462.144147
## iter 160 value 442.590446
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## iter 180 value 414.627835
## iter 190 value 403.766862
## iter 200 value 391.667554
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## iter 260 value 375.730019
## iter 270 value 367.811281
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## iter 300 value 342.886314
## iter 310 value 336.106750
## iter 320 value 327.551536
## iter 330 value 317.230931
## iter 340 value 309.049753
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## iter 400 value 301.415839
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## iter 460 value 300.819231
## iter 470 value 300.794936
## iter 480 value 300.767890
## iter 490 value 300.731218
## iter 500 value 300.704849
## final value 300.704849
## stopped after 500 iterations
## # weights: 141
## initial value 1485286.129457
## iter 10 value 1403.251952
## iter 20 value 1034.048557
## iter 30 value 906.531608
## iter 40 value 828.173311
## iter 50 value 753.118025
## iter 60 value 689.152492
## iter 70 value 646.366669
## iter 80 value 602.039234
## iter 90 value 559.905052
## iter 100 value 511.450492
## iter 110 value 464.840846
## iter 120 value 439.666439
## iter 130 value 417.329673
## iter 140 value 397.630989
## iter 150 value 388.131968
## iter 160 value 376.564218
## iter 170 value 367.829786
## iter 180 value 356.070019
## iter 190 value 343.605139
## iter 200 value 329.143077
## iter 210 value 317.602004
## iter 220 value 310.053088
## iter 230 value 300.796864
## iter 240 value 294.137987
## iter 250 value 282.768872
## iter 260 value 277.202262
## iter 270 value 273.950637
## iter 280 value 266.753687
## iter 290 value 261.536814
## iter 300 value 259.928962
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## iter 320 value 255.228433
## iter 330 value 252.829330
## iter 340 value 249.950407
## iter 350 value 244.149118
## iter 360 value 238.863935
## iter 370 value 234.341308
## iter 380 value 227.984255
## iter 390 value 219.850093
## iter 400 value 212.599590
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## iter 430 value 199.481753
## iter 440 value 196.502434
## iter 450 value 195.118177
## iter 460 value 194.367560
## iter 470 value 193.021333
## iter 480 value 191.509376
## iter 490 value 191.246782
## iter 500 value 190.922837
## final value 190.922837
## stopped after 500 iterations
## # weights: 15
## initial value 1393833.087880
## iter 10 value 9255.145350
## iter 20 value 6964.060991
## iter 30 value 5154.555610
## iter 40 value 4096.000374
## iter 50 value 3383.560369
## iter 60 value 2212.645230
## iter 70 value 1630.596245
## iter 80 value 1483.702580
## iter 90 value 1262.396080
## iter 100 value 1195.805107
## iter 110 value 1191.271052
## iter 120 value 1175.860179
## iter 130 value 1169.139250
## iter 140 value 1168.833853
## iter 150 value 1164.432615
## iter 160 value 1162.360653
## iter 170 value 1162.193309
## iter 180 value 1161.026879
## iter 190 value 1159.804129
## iter 200 value 1159.615471
## iter 210 value 1159.216867
## iter 220 value 1158.786125
## iter 230 value 1158.722283
## iter 240 value 1158.393469
## iter 250 value 1158.254385
## iter 260 value 1158.118151
## iter 270 value 1158.025459
## iter 280 value 1157.880653
## iter 290 value 1157.720970
## iter 300 value 1157.718826
## final value 1157.718716
## converged
## # weights: 36
## initial value 1436950.324028
## iter 10 value 37696.048330
## iter 20 value 22008.779316
## iter 30 value 7376.174966
## iter 40 value 4050.017833
## iter 50 value 2071.501161
## iter 60 value 1723.824610
## iter 70 value 1500.510096
## iter 80 value 1451.071267
## iter 90 value 1386.689232
## iter 100 value 1237.043663
## iter 110 value 1136.803726
## iter 120 value 1118.050103
## iter 130 value 1103.988324
## iter 140 value 1083.786702
## iter 150 value 1065.434774
## iter 160 value 1061.956556
## iter 170 value 1054.231372
## iter 180 value 1046.067425
## iter 190 value 1043.519932
## iter 200 value 1039.888204
## iter 210 value 1037.693830
## iter 220 value 1034.989921
## iter 230 value 1034.661807
## iter 240 value 1034.310702
## iter 250 value 1025.715255
## iter 260 value 1018.731726
## iter 270 value 1015.602075
## iter 280 value 1014.959221
## iter 290 value 1014.306198
## iter 300 value 1014.218162
## iter 310 value 1014.125936
## iter 320 value 1013.614775
## iter 330 value 1013.410692
## iter 340 value 1013.092562
## iter 350 value 1012.915281
## iter 360 value 1012.844053
## iter 370 value 1012.764815
## iter 380 value 1012.755096
## iter 390 value 1012.667675
## iter 400 value 1012.645304
## iter 410 value 1012.513499
## iter 420 value 1012.343160
## iter 430 value 1012.197155
## iter 440 value 1012.081758
## iter 450 value 1012.059322
## iter 460 value 1011.995206
## iter 470 value 1011.634320
## iter 480 value 1011.560636
## iter 490 value 1011.486943
## iter 500 value 1011.448402
## final value 1011.448402
## stopped after 500 iterations
## # weights: 71
## initial value 1373795.183354
## iter 10 value 1467.671411
## iter 20 value 1190.361563
## iter 30 value 1058.369983
## iter 40 value 973.855350
## iter 50 value 931.853458
## iter 60 value 910.870881
## iter 70 value 886.244227
## iter 80 value 867.873660
## iter 90 value 851.005555
## iter 100 value 817.233794
## iter 110 value 772.686890
## iter 120 value 749.218785
## iter 130 value 735.352059
## iter 140 value 716.572771
## iter 150 value 699.188656
## iter 160 value 690.381305
## iter 170 value 678.435008
## iter 180 value 660.602861
## iter 190 value 635.974079
## iter 200 value 613.493874
## iter 210 value 602.539371
## iter 220 value 599.064054
## iter 230 value 598.211614
## iter 240 value 596.578873
## iter 250 value 594.386557
## iter 260 value 590.606488
## iter 270 value 589.295301
## iter 280 value 589.041488
## iter 290 value 584.391163
## iter 300 value 578.015877
## iter 310 value 572.985175
## iter 320 value 570.728695
## iter 330 value 570.215283
## iter 340 value 567.625708
## iter 350 value 563.061997
## iter 360 value 560.135472
## iter 370 value 560.100963
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## iter 390 value 559.412550
## iter 400 value 559.091334
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## iter 430 value 559.061609
## iter 440 value 559.060387
## iter 450 value 559.059577
## iter 460 value 559.057772
## iter 470 value 559.030101
## iter 480 value 559.010226
## iter 490 value 558.869629
## iter 500 value 558.867047
## final value 558.867047
## stopped after 500 iterations
## # weights: 106
## initial value 1422508.792575
## iter 10 value 1604.229250
## iter 20 value 1144.741412
## iter 30 value 1023.547140
## iter 40 value 847.057535
## iter 50 value 762.110977
## iter 60 value 722.613225
## iter 70 value 659.191233
## iter 80 value 599.539229
## iter 90 value 562.358290
## iter 100 value 530.829342
## iter 110 value 503.809161
## iter 120 value 484.532128
## iter 130 value 472.285318
## iter 140 value 455.175128
## iter 150 value 441.172852
## iter 160 value 428.221210
## iter 170 value 417.075854
## iter 180 value 405.347577
## iter 190 value 387.174953
## iter 200 value 365.246886
## iter 210 value 352.059141
## iter 220 value 345.813112
## iter 230 value 343.433402
## iter 240 value 341.587657
## iter 250 value 337.439209
## iter 260 value 334.009899
## iter 270 value 332.205157
## iter 280 value 328.075796
## iter 290 value 318.789430
## iter 300 value 313.849969
## iter 310 value 310.504834
## iter 320 value 309.254215
## iter 330 value 307.750742
## iter 340 value 307.182611
## iter 350 value 306.800059
## iter 360 value 306.242161
## iter 370 value 305.526966
## iter 380 value 305.273011
## iter 390 value 305.032022
## iter 400 value 304.876989
## iter 410 value 304.776477
## iter 420 value 304.669582
## iter 430 value 304.576645
## iter 440 value 304.571252
## iter 450 value 304.562718
## iter 460 value 304.550529
## iter 470 value 304.538611
## iter 480 value 304.516555
## iter 490 value 304.451126
## iter 500 value 304.374046
## final value 304.374046
## stopped after 500 iterations
## # weights: 141
## initial value 1419651.917360
## iter 10 value 3742.707389
## iter 20 value 1844.516600
## iter 30 value 1233.778202
## iter 40 value 924.647094
## iter 50 value 816.118792
## iter 60 value 745.861086
## iter 70 value 686.694500
## iter 80 value 594.468171
## iter 90 value 549.466363
## iter 100 value 521.653829
## iter 110 value 504.165182
## iter 120 value 493.109285
## iter 130 value 483.802735
## iter 140 value 472.122369
## iter 150 value 455.490005
## iter 160 value 441.446178
## iter 170 value 433.127112
## iter 180 value 425.325851
## iter 190 value 402.123044
## iter 200 value 392.430410
## iter 210 value 387.563750
## iter 220 value 383.510923
## iter 230 value 380.680133
## iter 240 value 377.018863
## iter 250 value 373.520346
## iter 260 value 370.286429
## iter 270 value 367.760668
## iter 280 value 366.022591
## iter 290 value 364.951206
## iter 300 value 364.529219
## iter 310 value 364.072646
## iter 320 value 362.820390
## iter 330 value 361.037193
## iter 340 value 357.088143
## iter 350 value 353.362841
## iter 360 value 348.184167
## iter 370 value 343.094806
## iter 380 value 338.114852
## iter 390 value 333.439967
## iter 400 value 330.706842
## iter 410 value 328.623617
## iter 420 value 326.142137
## iter 430 value 323.642450
## iter 440 value 321.360963
## iter 450 value 318.926690
## iter 460 value 317.549878
## iter 470 value 316.661972
## iter 480 value 315.690607
## iter 490 value 314.368311
## iter 500 value 313.486442
## final value 313.486442
## stopped after 500 iterations
## # weights: 15
## initial value 1428285.941801
## iter 10 value 10367.997099
## iter 20 value 5384.233863
## iter 30 value 4733.764398
## iter 40 value 4179.718259
## iter 50 value 3515.064505
## iter 60 value 2490.210590
## iter 70 value 1695.148651
## iter 80 value 1440.748098
## iter 90 value 1439.260211
## iter 100 value 1402.164175
## iter 110 value 1390.301325
## iter 120 value 1388.883047
## iter 130 value 1385.512091
## iter 140 value 1380.777015
## iter 150 value 1380.254933
## iter 160 value 1379.884817
## iter 170 value 1377.159901
## iter 180 value 1376.390297
## iter 190 value 1376.321551
## iter 200 value 1375.136304
## iter 210 value 1374.761578
## iter 220 value 1374.754498
## iter 230 value 1374.669136
## iter 240 value 1373.777081
## iter 250 value 1373.742389
## iter 260 value 1373.737753
## iter 270 value 1373.455007
## iter 280 value 1373.156505
## iter 290 value 1373.152758
## iter 300 value 1373.050846
## iter 310 value 1372.676770
## iter 320 value 1372.667689
## final value 1372.666981
## converged
## # weights: 36
## initial value 1416048.944683
## iter 10 value 33595.315372
## iter 20 value 11697.134805
## iter 30 value 7586.798075
## iter 40 value 4967.783918
## iter 50 value 2609.581764
## iter 60 value 1764.424172
## iter 70 value 1424.076549
## iter 80 value 1416.168105
## iter 90 value 1413.657843
## iter 100 value 1391.616466
## iter 110 value 1385.197236
## iter 120 value 1372.459027
## iter 130 value 1365.952730
## iter 140 value 1359.989422
## iter 150 value 1356.785347
## iter 160 value 1353.201929
## iter 170 value 1349.859029
## iter 180 value 1343.542688
## iter 190 value 1312.963527
## iter 200 value 1284.163019
## iter 210 value 1252.490890
## iter 220 value 1232.840004
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## iter 240 value 1064.279027
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## iter 320 value 971.956924
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## iter 400 value 965.135688
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## iter 420 value 964.855489
## iter 430 value 964.810530
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## iter 450 value 961.911040
## iter 460 value 961.858375
## iter 470 value 961.402432
## iter 480 value 960.952871
## iter 490 value 960.900232
## iter 500 value 960.884811
## final value 960.884811
## stopped after 500 iterations
## # weights: 71
## initial value 1423448.721199
## iter 10 value 3356.214228
## iter 20 value 1436.194580
## iter 30 value 1115.659147
## iter 40 value 993.275726
## iter 50 value 896.233405
## iter 60 value 854.386689
## iter 70 value 789.048796
## iter 80 value 715.556647
## iter 90 value 677.847337
## iter 100 value 662.108900
## iter 110 value 648.741383
## iter 120 value 635.352515
## iter 130 value 627.995084
## iter 140 value 624.663383
## iter 150 value 622.988015
## iter 160 value 622.451509
## iter 170 value 621.889932
## iter 180 value 620.366400
## iter 190 value 618.109861
## iter 200 value 608.176236
## iter 210 value 595.586930
## iter 220 value 578.130884
## iter 230 value 570.324218
## iter 240 value 561.531646
## iter 250 value 540.408310
## iter 260 value 534.732373
## iter 270 value 531.233066
## iter 280 value 529.224991
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## iter 300 value 527.519890
## iter 310 value 527.280860
## iter 320 value 526.790157
## iter 330 value 525.949117
## iter 340 value 525.798255
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## iter 360 value 524.204819
## iter 370 value 520.281685
## iter 380 value 518.079267
## iter 390 value 517.085005
## iter 400 value 517.069058
## iter 410 value 517.057983
## iter 420 value 517.049201
## iter 430 value 516.946065
## iter 440 value 516.898517
## final value 516.898413
## converged
## # weights: 106
## initial value 1431069.838444
## iter 10 value 1605.362992
## iter 20 value 1038.472955
## iter 30 value 889.850918
## iter 40 value 810.627389
## iter 50 value 765.594627
## iter 60 value 731.579156
## iter 70 value 677.401983
## iter 80 value 642.761799
## iter 90 value 610.444947
## iter 100 value 598.230680
## iter 110 value 586.031659
## iter 120 value 552.625311
## iter 130 value 525.534414
## iter 140 value 505.930038
## iter 150 value 484.843288
## iter 160 value 470.944617
## iter 170 value 457.764296
## iter 180 value 444.370626
## iter 190 value 427.927605
## iter 200 value 411.586044
## iter 210 value 402.896423
## iter 220 value 400.061325
## iter 230 value 398.425777
## iter 240 value 396.308668
## iter 250 value 393.793231
## iter 260 value 391.135145
## iter 270 value 387.873003
## iter 280 value 385.137908
## iter 290 value 379.031102
## iter 300 value 368.587815
## iter 310 value 360.802257
## iter 320 value 356.824018
## iter 330 value 353.533179
## iter 340 value 348.740488
## iter 350 value 342.562836
## iter 360 value 333.505736
## iter 370 value 324.350102
## iter 380 value 320.586783
## iter 390 value 318.125385
## iter 400 value 315.847213
## iter 410 value 314.314988
## iter 420 value 312.608369
## iter 430 value 312.000039
## iter 440 value 311.964930
## iter 450 value 311.896058
## iter 460 value 311.781143
## iter 470 value 311.598088
## iter 480 value 311.019901
## iter 490 value 310.304530
## iter 500 value 308.603216
## final value 308.603216
## stopped after 500 iterations
## # weights: 141
## initial value 1415086.305566
## iter 10 value 1539.025169
## iter 20 value 1073.999351
## iter 30 value 895.746897
## iter 40 value 802.385289
## iter 50 value 714.426882
## iter 60 value 643.529536
## iter 70 value 585.430611
## iter 80 value 547.714703
## iter 90 value 517.931982
## iter 100 value 493.573022
## iter 110 value 473.072988
## iter 120 value 456.125464
## iter 130 value 436.941000
## iter 140 value 411.679932
## iter 150 value 391.803946
## iter 160 value 369.232702
## iter 170 value 348.776458
## iter 180 value 333.787354
## iter 190 value 323.835368
## iter 200 value 312.685920
## iter 210 value 303.662047
## iter 220 value 296.109205
## iter 230 value 290.121973
## iter 240 value 285.850570
## iter 250 value 282.538825
## iter 260 value 280.079597
## iter 270 value 276.903926
## iter 280 value 274.047946
## iter 290 value 272.483554
## iter 300 value 271.585217
## iter 310 value 269.184889
## iter 320 value 266.877628
## iter 330 value 263.078652
## iter 340 value 260.688584
## iter 350 value 258.228147
## iter 360 value 254.034513
## iter 370 value 250.502413
## iter 380 value 247.625926
## iter 390 value 244.496228
## iter 400 value 241.348157
## iter 410 value 239.246278
## iter 420 value 237.087519
## iter 430 value 236.107795
## iter 440 value 234.753297
## iter 450 value 232.660902
## iter 460 value 229.205451
## iter 470 value 226.515100
## iter 480 value 224.522724
## iter 490 value 222.747790
## iter 500 value 221.469696
## final value 221.469696
## stopped after 500 iterations
## # weights: 15
## initial value 1394295.921982
## iter 10 value 15725.178140
## iter 20 value 6950.118426
## iter 30 value 3527.563316
## iter 40 value 2926.561664
## iter 50 value 2082.149682
## iter 60 value 1945.248369
## iter 70 value 1726.936931
## iter 80 value 1626.058896
## iter 90 value 1600.555688
## iter 100 value 1491.584325
## iter 110 value 1430.487849
## iter 120 value 1426.914361
## iter 130 value 1417.844012
## iter 140 value 1417.608921
## final value 1417.605953
## converged
## # weights: 36
## initial value 1347134.598101
## iter 10 value 11028.784269
## iter 20 value 8797.130275
## iter 30 value 6663.104295
## iter 40 value 5565.344710
## iter 50 value 4672.154940
## iter 60 value 2762.743496
## iter 70 value 1907.016013
## iter 80 value 1510.882325
## iter 90 value 1352.205699
## iter 100 value 1242.162793
## iter 110 value 1140.369818
## iter 120 value 1114.828247
## iter 130 value 1093.160072
## iter 140 value 1089.654473
## iter 150 value 1081.250957
## iter 160 value 1074.681742
## iter 170 value 1068.314820
## iter 180 value 1060.152377
## iter 190 value 1057.823395
## iter 200 value 1054.824459
## iter 210 value 1054.120271
## iter 220 value 1053.890553
## iter 230 value 1053.668824
## iter 240 value 1053.626440
## iter 240 value 1053.626435
## iter 240 value 1053.626435
## final value 1053.626435
## converged
## # weights: 71
## initial value 1407159.361970
## iter 10 value 2080.707622
## iter 20 value 1222.959048
## iter 30 value 1139.031292
## iter 40 value 1088.840644
## iter 50 value 1025.596729
## iter 60 value 992.682563
## iter 70 value 957.366108
## iter 80 value 947.218257
## iter 90 value 941.400262
## iter 100 value 932.749781
## iter 110 value 923.724345
## iter 120 value 922.083410
## iter 130 value 920.543013
## iter 140 value 918.743045
## iter 150 value 916.149909
## iter 160 value 912.622761
## iter 170 value 910.312965
## iter 180 value 908.758714
## iter 190 value 907.780680
## iter 200 value 905.829374
## iter 210 value 901.436797
## iter 220 value 899.730391
## iter 230 value 895.945485
## iter 240 value 892.002352
## iter 250 value 887.832319
## iter 260 value 883.059465
## iter 270 value 881.037156
## iter 280 value 880.369143
## iter 290 value 880.030669
## iter 300 value 879.997807
## iter 310 value 879.975875
## iter 320 value 879.961292
## iter 330 value 879.956959
## iter 340 value 879.955164
## final value 879.955030
## converged
## # weights: 106
## initial value 1486846.061428
## iter 10 value 2428.037016
## iter 20 value 1507.325134
## iter 30 value 1305.461535
## iter 40 value 1176.980462
## iter 50 value 1059.562469
## iter 60 value 999.999014
## iter 70 value 963.302151
## iter 80 value 933.666888
## iter 90 value 911.743282
## iter 100 value 894.599268
## iter 110 value 866.311955
## iter 120 value 851.952594
## iter 130 value 846.041335
## iter 140 value 837.459804
## iter 150 value 832.177343
## iter 160 value 828.741008
## iter 170 value 816.753892
## iter 180 value 802.135221
## iter 190 value 794.943347
## iter 200 value 779.809614
## iter 210 value 764.516860
## iter 220 value 749.593789
## iter 230 value 742.031569
## iter 240 value 734.787252
## iter 250 value 731.933168
## iter 260 value 730.611367
## iter 270 value 730.395485
## iter 280 value 730.305644
## iter 290 value 730.219317
## iter 300 value 729.277474
## iter 310 value 717.448151
## iter 320 value 695.859331
## iter 330 value 682.371751
## iter 340 value 675.831175
## iter 350 value 673.195289
## iter 360 value 670.744424
## iter 370 value 668.743232
## iter 380 value 667.929451
## iter 390 value 667.336008
## iter 400 value 666.732642
## iter 410 value 666.524212
## iter 420 value 666.483712
## iter 430 value 666.469360
## iter 440 value 666.467182
## iter 450 value 666.463400
## iter 460 value 666.458956
## iter 470 value 666.424632
## iter 480 value 666.310307
## iter 490 value 666.219338
## iter 500 value 666.188280
## final value 666.188280
## stopped after 500 iterations
## # weights: 141
## initial value 1455583.108312
## iter 10 value 2012.268587
## iter 20 value 1219.817389
## iter 30 value 1036.002579
## iter 40 value 926.844960
## iter 50 value 860.576797
## iter 60 value 799.217322
## iter 70 value 747.281953
## iter 80 value 725.615849
## iter 90 value 713.155563
## iter 100 value 701.083016
## iter 110 value 687.648890
## iter 120 value 677.301906
## iter 130 value 667.633994
## iter 140 value 655.099693
## iter 150 value 633.583338
## iter 160 value 618.453989
## iter 170 value 604.189942
## iter 180 value 589.909041
## iter 190 value 582.114882
## iter 200 value 577.626129
## iter 210 value 572.628799
## iter 220 value 570.313465
## iter 230 value 568.502208
## iter 240 value 567.216887
## iter 250 value 566.071282
## iter 260 value 565.451764
## iter 270 value 565.025617
## iter 280 value 564.733917
## iter 290 value 564.320006
## iter 300 value 563.493828
## iter 310 value 561.614624
## iter 320 value 559.379847
## iter 330 value 557.426177
## iter 340 value 555.927147
## iter 350 value 554.204455
## iter 360 value 551.230347
## iter 370 value 547.665028
## iter 380 value 545.503561
## iter 390 value 544.376962
## iter 400 value 543.851102
## iter 410 value 543.657720
## iter 420 value 543.588437
## iter 430 value 543.557833
## iter 440 value 543.554129
## final value 543.554103
## converged
## # weights: 15
## initial value 1421575.281458
## iter 10 value 23729.334805
## iter 20 value 5960.228677
## iter 30 value 4062.201322
## iter 40 value 3330.827421
## iter 50 value 2292.319589
## iter 60 value 1666.032762
## iter 70 value 1408.885441
## iter 80 value 1361.494750
## iter 90 value 1211.854616
## iter 100 value 1171.792701
## iter 110 value 1169.600978
## iter 120 value 1161.663768
## iter 130 value 1155.590907
## iter 140 value 1154.957299
## iter 150 value 1150.924073
## iter 160 value 1144.617864
## iter 170 value 1143.256593
## iter 180 value 1140.571872
## iter 190 value 1139.691216
## iter 200 value 1139.288432
## iter 210 value 1138.383071
## iter 220 value 1137.841924
## iter 230 value 1137.772143
## final value 1137.772117
## converged
## # weights: 36
## initial value 1381766.120345
## iter 10 value 58309.046647
## iter 20 value 24699.633022
## iter 30 value 4995.195262
## iter 40 value 3354.017372
## iter 50 value 2117.263207
## iter 60 value 1637.241474
## iter 70 value 1628.090930
## iter 80 value 1596.015705
## iter 90 value 1577.414122
## iter 100 value 1544.711459
## iter 110 value 1499.878531
## iter 120 value 1456.151384
## iter 130 value 1412.051719
## iter 140 value 1397.126111
## iter 150 value 1330.213821
## iter 160 value 1238.856298
## iter 170 value 1125.587019
## iter 180 value 1114.735682
## iter 190 value 1111.887815
## iter 200 value 1106.885945
## iter 210 value 1095.507826
## iter 220 value 1053.777491
## iter 230 value 1045.287486
## iter 240 value 1027.479572
## iter 250 value 991.520845
## iter 260 value 982.516983
## iter 270 value 979.950008
## iter 280 value 977.760914
## iter 290 value 976.924834
## iter 300 value 976.847138
## iter 310 value 976.164178
## iter 320 value 975.700124
## iter 330 value 975.273489
## iter 340 value 975.230589
## iter 350 value 975.223833
## final value 975.221511
## converged
## # weights: 71
## initial value 1423539.659459
## iter 10 value 1315.407119
## iter 20 value 1119.872470
## iter 30 value 1040.580066
## iter 40 value 941.801030
## iter 50 value 867.339600
## iter 60 value 797.694983
## iter 70 value 773.720546
## iter 80 value 759.437874
## iter 90 value 744.004492
## iter 100 value 709.861428
## iter 110 value 672.383373
## iter 120 value 652.237052
## iter 130 value 643.158000
## iter 140 value 632.523104
## iter 150 value 628.512726
## iter 160 value 626.310259
## iter 170 value 620.812824
## iter 180 value 617.086361
## iter 190 value 614.386084
## iter 200 value 611.584964
## iter 210 value 608.066207
## iter 220 value 599.674097
## iter 230 value 587.187145
## iter 240 value 579.532699
## iter 250 value 576.012792
## iter 260 value 574.392626
## iter 270 value 570.711436
## iter 280 value 563.364715
## iter 290 value 561.713256
## iter 300 value 561.616435
## iter 310 value 561.470057
## iter 320 value 561.332192
## iter 330 value 561.203690
## iter 340 value 561.056547
## iter 350 value 559.451447
## iter 360 value 547.223586
## iter 370 value 534.521349
## iter 380 value 527.792883
## iter 390 value 526.873359
## iter 400 value 526.542555
## iter 410 value 526.481222
## iter 420 value 526.445360
## iter 430 value 526.436523
## iter 440 value 526.436028
## final value 526.435531
## converged
## # weights: 106
## initial value 1385946.694552
## iter 10 value 1283.998820
## iter 20 value 964.161015
## iter 30 value 864.904072
## iter 40 value 774.064934
## iter 50 value 720.915858
## iter 60 value 682.739131
## iter 70 value 646.348118
## iter 80 value 598.892153
## iter 90 value 569.273105
## iter 100 value 544.770145
## iter 110 value 524.881710
## iter 120 value 504.345923
## iter 130 value 489.064650
## iter 140 value 478.040632
## iter 150 value 465.109342
## iter 160 value 449.925572
## iter 170 value 435.114422
## iter 180 value 421.232060
## iter 190 value 413.849897
## iter 200 value 408.537927
## iter 210 value 404.036039
## iter 220 value 402.072013
## iter 230 value 400.150674
## iter 240 value 397.703607
## iter 250 value 393.044459
## iter 260 value 385.469218
## iter 270 value 372.621015
## iter 280 value 362.681109
## iter 290 value 357.163551
## iter 300 value 352.397977
## iter 310 value 345.030064
## iter 320 value 338.361856
## iter 330 value 334.757936
## iter 340 value 330.877586
## iter 350 value 328.249082
## iter 360 value 326.055109
## iter 370 value 324.699534
## iter 380 value 321.792709
## iter 390 value 315.113484
## iter 400 value 311.605808
## iter 410 value 309.652467
## iter 420 value 305.964070
## iter 430 value 303.907112
## iter 440 value 303.827274
## iter 450 value 303.714376
## iter 460 value 303.517572
## iter 470 value 303.330378
## iter 480 value 302.659422
## iter 490 value 301.533068
## iter 500 value 300.691594
## final value 300.691594
## stopped after 500 iterations
## # weights: 141
## initial value 1384494.474104
## iter 10 value 1652.988125
## iter 20 value 1098.069740
## iter 30 value 909.530477
## iter 40 value 785.522430
## iter 50 value 684.955771
## iter 60 value 613.888064
## iter 70 value 569.088664
## iter 80 value 526.313456
## iter 90 value 503.294104
## iter 100 value 482.748706
## iter 110 value 456.169031
## iter 120 value 431.360694
## iter 130 value 401.683290
## iter 140 value 375.385200
## iter 150 value 355.694948
## iter 160 value 342.281825
## iter 170 value 327.152694
## iter 180 value 317.006709
## iter 190 value 304.036521
## iter 200 value 286.099453
## iter 210 value 273.025805
## iter 220 value 264.809402
## iter 230 value 257.300104
## iter 240 value 252.933665
## iter 250 value 250.299941
## iter 260 value 248.439154
## iter 270 value 247.064123
## iter 280 value 245.373208
## iter 290 value 244.405831
## iter 300 value 243.509478
## iter 310 value 241.511481
## iter 320 value 239.037404
## iter 330 value 234.286909
## iter 340 value 230.584621
## iter 350 value 226.853420
## iter 360 value 223.307497
## iter 370 value 219.637544
## iter 380 value 217.163782
## iter 390 value 214.028496
## iter 400 value 209.624619
## iter 410 value 205.385698
## iter 420 value 203.932566
## iter 430 value 203.280752
## iter 440 value 202.543652
## iter 450 value 201.599221
## iter 460 value 200.066378
## iter 470 value 199.483043
## iter 480 value 199.328437
## iter 490 value 199.250164
## iter 500 value 199.206387
## final value 199.206387
## stopped after 500 iterations
## # weights: 15
## initial value 1389490.039765
## iter 10 value 14324.688282
## iter 20 value 5876.939923
## iter 30 value 4517.780257
## iter 40 value 3059.115888
## iter 50 value 2208.336595
## iter 60 value 1676.424631
## iter 70 value 1592.749486
## iter 80 value 1575.703559
## iter 90 value 1563.508494
## iter 100 value 1558.128344
## iter 110 value 1556.667604
## iter 120 value 1555.986945
## iter 130 value 1554.746909
## iter 140 value 1553.860923
## iter 150 value 1553.856083
## final value 1553.856003
## converged
## # weights: 36
## initial value 1400210.689614
## iter 10 value 11616.858464
## iter 20 value 11380.201817
## iter 30 value 11378.880691
## iter 40 value 11365.829135
## iter 50 value 11297.145617
## iter 60 value 10939.293155
## iter 70 value 9928.053934
## iter 80 value 7624.680954
## iter 90 value 4483.206544
## iter 100 value 2321.367049
## iter 110 value 1733.572420
## iter 120 value 1697.812244
## iter 130 value 1673.129667
## iter 140 value 1633.702204
## iter 150 value 1601.383995
## iter 160 value 1599.592955
## iter 170 value 1592.770646
## iter 180 value 1584.332821
## iter 190 value 1563.978933
## iter 200 value 1466.916266
## iter 210 value 1349.980825
## iter 220 value 1250.204052
## iter 230 value 1174.970879
## iter 240 value 1100.489867
## iter 250 value 998.437449
## iter 260 value 980.413877
## iter 270 value 967.451152
## iter 280 value 955.524788
## iter 290 value 954.054883
## iter 300 value 953.365800
## iter 310 value 949.584130
## iter 320 value 946.209705
## iter 330 value 945.438851
## iter 340 value 944.011329
## iter 350 value 943.122059
## iter 360 value 942.594361
## iter 370 value 942.517112
## iter 380 value 942.304948
## iter 390 value 942.165345
## iter 400 value 941.896572
## iter 410 value 941.795510
## iter 420 value 940.207582
## iter 430 value 934.935149
## iter 440 value 926.876531
## iter 450 value 910.952736
## iter 460 value 909.738978
## iter 470 value 909.576787
## iter 480 value 909.322225
## iter 490 value 908.962058
## iter 500 value 908.960986
## final value 908.960986
## stopped after 500 iterations
## # weights: 71
## initial value 1442226.048993
## iter 10 value 1226.933238
## iter 20 value 1072.830007
## iter 30 value 995.841818
## iter 40 value 932.787010
## iter 50 value 867.421080
## iter 60 value 795.313368
## iter 70 value 739.715865
## iter 80 value 705.987135
## iter 90 value 695.743869
## iter 100 value 688.699032
## iter 110 value 677.839879
## iter 120 value 672.600083
## iter 130 value 668.551755
## iter 140 value 664.636235
## iter 150 value 661.115373
## iter 160 value 657.773261
## iter 170 value 652.088244
## iter 180 value 643.287009
## iter 190 value 635.807584
## iter 200 value 624.115497
## iter 210 value 614.957016
## iter 220 value 610.401177
## iter 230 value 608.533935
## iter 240 value 606.347678
## iter 250 value 601.839140
## iter 260 value 598.744613
## iter 270 value 596.336352
## iter 280 value 594.190099
## iter 290 value 592.567980
## iter 300 value 591.980096
## iter 310 value 591.317805
## iter 320 value 589.723147
## iter 330 value 586.489483
## iter 340 value 576.796831
## iter 350 value 552.218509
## iter 360 value 541.873961
## iter 370 value 535.825268
## iter 380 value 533.092012
## iter 390 value 529.857548
## iter 400 value 527.451420
## iter 410 value 526.830642
## iter 420 value 526.608827
## iter 430 value 526.490946
## iter 440 value 526.487232
## iter 450 value 526.485257
## iter 460 value 526.476242
## iter 470 value 526.464431
## iter 480 value 526.454168
## iter 490 value 526.439074
## iter 500 value 526.382879
## final value 526.382879
## stopped after 500 iterations
## # weights: 106
## initial value 1391625.891468
## iter 10 value 1396.734866
## iter 20 value 1094.834159
## iter 30 value 951.118649
## iter 40 value 857.573586
## iter 50 value 758.462567
## iter 60 value 724.614265
## iter 70 value 666.769211
## iter 80 value 599.710709
## iter 90 value 554.485299
## iter 100 value 531.967361
## iter 110 value 515.450617
## iter 120 value 494.677584
## iter 130 value 475.551733
## iter 140 value 455.888794
## iter 150 value 439.881061
## iter 160 value 415.063318
## iter 170 value 401.734263
## iter 180 value 389.852756
## iter 190 value 379.956923
## iter 200 value 373.362452
## iter 210 value 360.249224
## iter 220 value 357.084233
## iter 230 value 354.454981
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## iter 480 value 301.308576
## iter 490 value 301.303215
## iter 500 value 301.292486
## final value 301.292486
## stopped after 500 iterations
## # weights: 141
## initial value 1454602.489563
## iter 10 value 1371.006540
## iter 20 value 1064.883584
## iter 30 value 920.586935
## iter 40 value 832.871982
## iter 50 value 755.224488
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## iter 360 value 200.381043
## iter 370 value 197.569194
## iter 380 value 192.738914
## iter 390 value 189.310304
## iter 400 value 187.015938
## iter 410 value 185.022402
## iter 420 value 182.075660
## iter 430 value 178.169691
## iter 440 value 176.305641
## iter 450 value 174.420466
## iter 460 value 172.636490
## iter 470 value 171.318235
## iter 480 value 170.494596
## iter 490 value 169.570920
## iter 500 value 169.021540
## final value 169.021540
## stopped after 500 iterations
## # weights: 15
## initial value 1392144.189633
## iter 10 value 6433.812340
## iter 20 value 2676.120080
## iter 30 value 1811.927804
## iter 40 value 1682.292750
## iter 50 value 1476.332313
## iter 60 value 1315.111942
## iter 70 value 1274.292169
## iter 80 value 1224.564562
## iter 90 value 1160.704046
## iter 100 value 1146.391149
## iter 110 value 1143.077780
## iter 120 value 1138.916403
## iter 130 value 1137.219353
## iter 140 value 1136.078993
## iter 150 value 1133.626263
## iter 160 value 1132.813788
## iter 170 value 1132.331805
## iter 180 value 1132.009354
## iter 190 value 1131.632142
## iter 200 value 1131.453202
## iter 210 value 1131.224373
## final value 1130.792211
## converged
## # weights: 36
## initial value 1359729.007212
## iter 10 value 17691.428547
## iter 20 value 3955.154273
## iter 30 value 2937.702788
## iter 40 value 2024.870173
## iter 50 value 1712.111828
## iter 60 value 1512.661991
## iter 70 value 1393.587728
## iter 80 value 1307.075522
## iter 90 value 1261.002195
## iter 100 value 1154.092973
## iter 110 value 1044.166093
## iter 120 value 1003.069491
## iter 130 value 992.442906
## iter 140 value 980.014704
## iter 150 value 968.787261
## iter 160 value 966.132179
## iter 170 value 962.360079
## iter 180 value 958.594611
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## iter 210 value 943.328843
## iter 220 value 941.057242
## iter 230 value 940.661520
## iter 240 value 940.444540
## iter 250 value 938.988496
## iter 260 value 936.402156
## iter 270 value 934.280806
## iter 280 value 933.513869
## iter 290 value 932.854822
## iter 300 value 932.590950
## iter 310 value 932.462597
## iter 320 value 932.319529
## iter 330 value 931.955702
## iter 340 value 930.315471
## iter 350 value 929.575739
## iter 360 value 928.710537
## iter 370 value 928.018720
## iter 380 value 928.004621
## iter 390 value 927.992075
## iter 400 value 927.969310
## iter 410 value 927.289716
## iter 420 value 926.803561
## iter 430 value 926.156505
## iter 440 value 924.715305
## iter 450 value 924.337551
## iter 460 value 923.774407
## iter 470 value 920.706096
## iter 480 value 919.435904
## iter 490 value 917.840575
## iter 500 value 915.410804
## final value 915.410804
## stopped after 500 iterations
## # weights: 71
## initial value 1452113.879510
## iter 10 value 16491.729511
## iter 20 value 12327.550742
## iter 30 value 8290.850180
## iter 40 value 5453.143542
## iter 50 value 3034.536798
## iter 60 value 1477.509172
## iter 70 value 1181.044809
## iter 80 value 1108.703281
## iter 90 value 1071.841831
## iter 100 value 1058.282704
## iter 110 value 1038.735706
## iter 120 value 1027.336030
## iter 130 value 1014.316595
## iter 140 value 1005.312422
## iter 150 value 999.091054
## iter 160 value 994.553662
## iter 170 value 990.532483
## iter 180 value 988.559533
## iter 190 value 984.556771
## iter 200 value 977.588167
## iter 210 value 966.704296
## iter 220 value 957.247276
## iter 230 value 949.341368
## iter 240 value 947.068849
## iter 250 value 946.101001
## iter 260 value 942.948448
## iter 270 value 940.805811
## iter 280 value 940.424509
## iter 290 value 940.227769
## iter 300 value 939.835766
## iter 310 value 938.922172
## iter 320 value 938.684092
## iter 330 value 938.289945
## iter 340 value 937.496729
## iter 350 value 936.541986
## iter 360 value 935.175009
## iter 370 value 933.424881
## iter 380 value 933.157244
## iter 390 value 933.056751
## iter 400 value 932.682537
## iter 410 value 932.488279
## iter 420 value 932.179834
## iter 430 value 931.870476
## iter 440 value 931.653296
## iter 450 value 931.492243
## iter 460 value 931.378489
## iter 470 value 931.004088
## iter 480 value 930.747140
## iter 490 value 930.641320
## iter 500 value 930.595132
## final value 930.595132
## stopped after 500 iterations
## # weights: 106
## initial value 1363546.514924
## iter 10 value 1303.333578
## iter 20 value 1003.741511
## iter 30 value 898.282158
## iter 40 value 829.601040
## iter 50 value 773.891295
## iter 60 value 726.391702
## iter 70 value 676.355824
## iter 80 value 640.657594
## iter 90 value 618.890422
## iter 100 value 577.933925
## iter 110 value 539.051787
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## iter 160 value 445.044131
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## iter 190 value 411.927311
## iter 200 value 405.169824
## iter 210 value 394.693626
## iter 220 value 388.628775
## iter 230 value 385.675041
## iter 240 value 378.918758
## iter 250 value 368.577004
## iter 260 value 362.168470
## iter 270 value 358.706260
## iter 280 value 352.601800
## iter 290 value 348.672214
## iter 300 value 344.775145
## iter 310 value 339.740245
## iter 320 value 337.274193
## iter 330 value 332.083732
## iter 340 value 324.286479
## iter 350 value 320.604535
## iter 360 value 318.145984
## iter 370 value 316.806642
## iter 380 value 315.764522
## iter 390 value 314.830735
## iter 400 value 313.947746
## iter 410 value 313.437142
## iter 420 value 313.004588
## iter 430 value 312.643572
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## iter 450 value 312.535936
## iter 460 value 312.489478
## iter 470 value 312.453986
## iter 480 value 312.331695
## iter 490 value 312.230971
## iter 500 value 312.205064
## final value 312.205064
## stopped after 500 iterations
## # weights: 141
## initial value 1378619.868739
## iter 10 value 1858.928226
## iter 20 value 1062.907500
## iter 30 value 935.561001
## iter 40 value 870.301321
## iter 50 value 759.180047
## iter 60 value 693.564592
## iter 70 value 637.995490
## iter 80 value 586.961667
## iter 90 value 541.337192
## iter 100 value 501.698879
## iter 110 value 468.100924
## iter 120 value 450.795621
## iter 130 value 433.122732
## iter 140 value 415.159483
## iter 150 value 395.681592
## iter 160 value 383.212258
## iter 170 value 367.002818
## iter 180 value 347.899697
## iter 190 value 334.994893
## iter 200 value 324.765103
## iter 210 value 316.048628
## iter 220 value 306.366314
## iter 230 value 300.964829
## iter 240 value 297.578954
## iter 250 value 293.490389
## iter 260 value 288.899638
## iter 270 value 285.207792
## iter 280 value 281.116034
## iter 290 value 279.382592
## iter 300 value 277.948865
## iter 310 value 276.208802
## iter 320 value 273.220517
## iter 330 value 270.325901
## iter 340 value 265.224281
## iter 350 value 259.221778
## iter 360 value 251.302235
## iter 370 value 243.879029
## iter 380 value 237.093860
## iter 390 value 229.293639
## iter 400 value 217.809341
## iter 410 value 209.193311
## iter 420 value 204.518701
## iter 430 value 200.814327
## iter 440 value 196.174469
## iter 450 value 193.063760
## iter 460 value 190.469498
## iter 470 value 186.892697
## iter 480 value 184.033892
## iter 490 value 182.229653
## iter 500 value 180.302852
## final value 180.302852
## stopped after 500 iterations
## # weights: 15
## initial value 1394897.212124
## iter 10 value 5162.713161
## iter 20 value 5078.609650
## iter 30 value 4801.203746
## iter 40 value 3074.250956
## iter 50 value 2154.882206
## iter 60 value 1924.421827
## iter 70 value 1870.734967
## iter 80 value 1834.852688
## iter 90 value 1823.743128
## iter 100 value 1818.571540
## iter 110 value 1818.536299
## final value 1818.536099
## converged
## # weights: 36
## initial value 1401282.851012
## iter 10 value 299377.549970
## iter 20 value 46719.334411
## iter 30 value 18825.354273
## iter 40 value 3922.768110
## iter 50 value 2157.894096
## iter 60 value 1731.840851
## iter 70 value 1456.286086
## iter 80 value 1338.321858
## iter 90 value 1232.041351
## iter 100 value 1130.596183
## iter 110 value 1019.648902
## iter 120 value 993.516137
## iter 130 value 974.765751
## iter 140 value 953.094140
## iter 150 value 949.117352
## iter 160 value 947.951398
## iter 170 value 942.732152
## iter 180 value 937.182200
## iter 190 value 929.935079
## iter 200 value 925.579572
## iter 210 value 922.699401
## iter 220 value 919.457786
## iter 230 value 919.134128
## iter 240 value 918.217481
## iter 250 value 917.894544
## iter 260 value 917.596083
## iter 270 value 916.856352
## iter 280 value 915.798906
## iter 290 value 914.423154
## iter 300 value 914.096331
## iter 310 value 914.003027
## iter 320 value 913.857292
## iter 330 value 913.802022
## iter 340 value 913.778654
## iter 350 value 913.450678
## iter 360 value 912.620186
## iter 370 value 912.186705
## iter 380 value 912.170843
## iter 390 value 912.001256
## iter 400 value 911.961998
## iter 410 value 911.936799
## iter 420 value 911.924235
## iter 430 value 911.888451
## iter 440 value 911.804263
## iter 450 value 911.797198
## iter 460 value 911.757936
## iter 470 value 911.749491
## iter 480 value 911.745796
## iter 490 value 911.735760
## iter 500 value 911.571557
## final value 911.571557
## stopped after 500 iterations
## # weights: 71
## initial value 1376017.317020
## iter 10 value 3464.612535
## iter 20 value 1751.165603
## iter 30 value 1422.929005
## iter 40 value 1117.303075
## iter 50 value 955.655064
## iter 60 value 888.847305
## iter 70 value 862.120361
## iter 80 value 839.958883
## iter 90 value 827.557725
## iter 100 value 820.683860
## iter 110 value 811.722289
## iter 120 value 804.313757
## iter 130 value 795.707076
## iter 140 value 785.468441
## iter 150 value 781.903630
## iter 160 value 779.800490
## iter 170 value 776.814668
## iter 180 value 770.670435
## iter 190 value 766.385559
## iter 200 value 762.837572
## iter 210 value 749.070654
## iter 220 value 744.160498
## iter 230 value 739.437877
## iter 240 value 736.551619
## iter 250 value 734.489056
## iter 260 value 733.560971
## iter 270 value 733.392458
## iter 280 value 733.145980
## iter 290 value 732.611996
## iter 300 value 732.570223
## iter 310 value 732.457219
## iter 320 value 732.338296
## iter 330 value 732.220644
## iter 340 value 731.745401
## iter 350 value 731.521834
## iter 360 value 731.517942
## iter 370 value 731.506487
## iter 380 value 731.504802
## iter 390 value 731.495499
## iter 400 value 731.461477
## iter 410 value 731.441174
## iter 420 value 731.427676
## iter 430 value 731.421643
## iter 440 value 731.421121
## iter 440 value 731.421116
## iter 440 value 731.421115
## final value 731.421115
## converged
## # weights: 106
## initial value 1407377.914299
## iter 10 value 2074.402667
## iter 20 value 1201.432719
## iter 30 value 1042.422082
## iter 40 value 923.938316
## iter 50 value 831.032753
## iter 60 value 710.205955
## iter 70 value 651.342813
## iter 80 value 615.889866
## iter 90 value 583.348638
## iter 100 value 529.034721
## iter 110 value 504.754408
## iter 120 value 487.981531
## iter 130 value 470.585053
## iter 140 value 460.747388
## iter 150 value 452.717126
## iter 160 value 443.235224
## iter 170 value 435.857171
## iter 180 value 421.020237
## iter 190 value 401.809648
## iter 200 value 393.972519
## iter 210 value 387.049904
## iter 220 value 383.657858
## iter 230 value 381.139890
## iter 240 value 378.160243
## iter 250 value 376.014390
## iter 260 value 372.217909
## iter 270 value 368.573342
## iter 280 value 365.808981
## iter 290 value 355.625908
## iter 300 value 351.126859
## iter 310 value 347.943010
## iter 320 value 343.015882
## iter 330 value 340.542429
## iter 340 value 337.334651
## iter 350 value 330.831954
## iter 360 value 325.399510
## iter 370 value 321.731734
## iter 380 value 319.617890
## iter 390 value 318.451430
## iter 400 value 316.694868
## iter 410 value 315.176868
## iter 420 value 314.780802
## iter 430 value 314.510744
## iter 440 value 314.495566
## iter 450 value 314.479296
## iter 460 value 314.444859
## iter 470 value 314.424583
## iter 480 value 314.406412
## iter 490 value 314.394975
## iter 500 value 314.069708
## final value 314.069708
## stopped after 500 iterations
## # weights: 141
## initial value 1388056.072870
## iter 10 value 1684.295365
## iter 20 value 1153.818933
## iter 30 value 982.515877
## iter 40 value 884.983327
## iter 50 value 791.116418
## iter 60 value 705.268872
## iter 70 value 649.254072
## iter 80 value 579.855527
## iter 90 value 527.283304
## iter 100 value 480.903862
## iter 110 value 446.935913
## iter 120 value 424.665189
## iter 130 value 399.135852
## iter 140 value 370.709539
## iter 150 value 357.444193
## iter 160 value 347.673558
## iter 170 value 335.178396
## iter 180 value 321.796647
## iter 190 value 311.954455
## iter 200 value 300.054588
## iter 210 value 288.263785
## iter 220 value 273.995826
## iter 230 value 266.532930
## iter 240 value 261.576882
## iter 250 value 255.931468
## iter 260 value 247.748924
## iter 270 value 240.945897
## iter 280 value 236.453058
## iter 290 value 234.334535
## iter 300 value 233.439519
## iter 310 value 231.159725
## iter 320 value 228.314014
## iter 330 value 224.902002
## iter 340 value 218.455239
## iter 350 value 207.358814
## iter 360 value 200.892706
## iter 370 value 197.616852
## iter 380 value 194.147778
## iter 390 value 190.508808
## iter 400 value 187.722129
## iter 410 value 183.688059
## iter 420 value 179.321355
## iter 430 value 173.733639
## iter 440 value 168.635558
## iter 450 value 163.646793
## iter 460 value 161.347154
## iter 470 value 159.199635
## iter 480 value 157.566174
## iter 490 value 154.744408
## iter 500 value 153.144467
## final value 153.144467
## stopped after 500 iterations
## # weights: 15
## initial value 1406384.173774
## iter 10 value 13770.535132
## iter 20 value 6442.126009
## iter 30 value 3961.337201
## iter 40 value 3530.230923
## iter 50 value 2129.021526
## iter 60 value 1642.019028
## iter 70 value 1609.446071
## iter 80 value 1560.165248
## iter 90 value 1527.023094
## iter 100 value 1524.871504
## iter 110 value 1522.102226
## iter 120 value 1521.694254
## final value 1521.694064
## converged
## # weights: 36
## initial value 1419345.938229
## iter 10 value 10285.485097
## iter 20 value 2133.955217
## iter 30 value 1585.293843
## iter 40 value 1290.235646
## iter 50 value 1233.916035
## iter 60 value 1208.393003
## iter 70 value 1195.516965
## iter 80 value 1188.829209
## iter 90 value 1185.736841
## iter 100 value 1176.333956
## iter 110 value 1170.663831
## iter 120 value 1161.185878
## iter 130 value 1150.371017
## iter 140 value 1143.791189
## iter 150 value 1141.248770
## iter 160 value 1140.060515
## iter 170 value 1137.697619
## iter 180 value 1136.325388
## iter 190 value 1134.972170
## iter 200 value 1129.388113
## iter 210 value 1127.405256
## iter 220 value 1127.364939
## iter 220 value 1127.364928
## final value 1127.364928
## converged
## # weights: 71
## initial value 1418208.299342
## iter 10 value 1511.385059
## iter 20 value 1201.424527
## iter 30 value 1106.939757
## iter 40 value 1034.609531
## iter 50 value 948.160662
## iter 60 value 890.552004
## iter 70 value 867.892470
## iter 80 value 849.904880
## iter 90 value 832.198211
## iter 100 value 816.405006
## iter 110 value 805.896390
## iter 120 value 797.676841
## iter 130 value 788.006387
## iter 140 value 783.980251
## iter 150 value 780.190004
## iter 160 value 777.333317
## iter 170 value 772.997289
## iter 180 value 769.450843
## iter 190 value 767.194185
## iter 200 value 765.620129
## iter 210 value 764.966629
## iter 220 value 764.893326
## iter 230 value 764.816231
## iter 240 value 763.216966
## iter 250 value 759.392214
## iter 260 value 755.597226
## iter 270 value 752.209103
## iter 280 value 750.418603
## iter 290 value 749.957810
## iter 300 value 749.910971
## iter 310 value 749.792740
## iter 320 value 749.710589
## iter 330 value 749.066642
## iter 340 value 745.329790
## iter 350 value 739.117790
## iter 360 value 738.097433
## iter 370 value 738.085150
## final value 738.085114
## converged
## # weights: 106
## initial value 1449985.119957
## iter 10 value 1758.890706
## iter 20 value 1214.730413
## iter 30 value 1045.211963
## iter 40 value 932.133679
## iter 50 value 886.510521
## iter 60 value 847.062979
## iter 70 value 810.493104
## iter 80 value 784.385695
## iter 90 value 765.576851
## iter 100 value 752.741255
## iter 110 value 738.455595
## iter 120 value 730.039813
## iter 130 value 716.952158
## iter 140 value 706.587225
## iter 150 value 697.176892
## iter 160 value 691.003893
## iter 170 value 686.186771
## iter 180 value 682.886178
## iter 190 value 678.854083
## iter 200 value 676.074763
## iter 210 value 673.910854
## iter 220 value 672.925219
## iter 230 value 670.982874
## iter 240 value 667.475003
## iter 250 value 665.475129
## iter 260 value 663.798610
## iter 270 value 662.945312
## iter 280 value 662.202662
## iter 290 value 659.282755
## iter 300 value 656.427126
## iter 310 value 646.784586
## iter 320 value 634.594268
## iter 330 value 626.846997
## iter 340 value 619.842910
## iter 350 value 617.088115
## iter 360 value 616.137738
## iter 370 value 615.775151
## iter 380 value 615.587091
## iter 390 value 615.289510
## iter 400 value 615.200831
## iter 410 value 615.177809
## iter 420 value 615.175197
## final value 615.174503
## converged
## # weights: 141
## initial value 1442019.296828
## iter 10 value 2135.762394
## iter 20 value 1245.768955
## iter 30 value 1055.972566
## iter 40 value 907.837633
## iter 50 value 813.404619
## iter 60 value 758.620533
## iter 70 value 718.241072
## iter 80 value 681.105110
## iter 90 value 663.018197
## iter 100 value 648.306951
## iter 110 value 636.832177
## iter 120 value 627.259535
## iter 130 value 619.276473
## iter 140 value 611.373841
## iter 150 value 605.894464
## iter 160 value 597.688702
## iter 170 value 593.212221
## iter 180 value 587.419283
## iter 190 value 583.198573
## iter 200 value 576.744091
## iter 210 value 570.306860
## iter 220 value 563.239511
## iter 230 value 550.595925
## iter 240 value 544.382092
## iter 250 value 537.447854
## iter 260 value 533.626267
## iter 270 value 527.581366
## iter 280 value 524.948526
## iter 290 value 523.439255
## iter 300 value 521.171975
## iter 310 value 517.264828
## iter 320 value 512.162345
## iter 330 value 505.485240
## iter 340 value 495.151632
## iter 350 value 488.985637
## iter 360 value 485.890829
## iter 370 value 484.029069
## iter 380 value 482.026200
## iter 390 value 478.993648
## iter 400 value 476.991262
## iter 410 value 475.273422
## iter 420 value 472.124650
## iter 430 value 469.299592
## iter 440 value 466.348386
## iter 450 value 464.065269
## iter 460 value 462.960269
## iter 470 value 461.886329
## iter 480 value 461.574195
## iter 490 value 461.484284
## iter 500 value 461.456913
## final value 461.456913
## stopped after 500 iterations
## # weights: 15
## initial value 1406748.916990
## iter 10 value 5344.778159
## iter 20 value 4171.474859
## iter 30 value 3762.020870
## iter 40 value 3591.519595
## iter 50 value 3169.823943
## iter 60 value 2755.674271
## iter 70 value 1931.314589
## iter 80 value 1591.791014
## iter 90 value 1409.387507
## iter 100 value 1331.425372
## iter 110 value 1270.718900
## iter 120 value 1260.124728
## iter 130 value 1258.479596
## iter 140 value 1252.912787
## iter 150 value 1249.355674
## iter 160 value 1249.161318
## iter 170 value 1248.235828
## iter 180 value 1247.843143
## iter 190 value 1247.828647
## iter 200 value 1247.804769
## final value 1247.804612
## converged
## # weights: 36
## initial value 1404654.402350
## iter 10 value 5467.228743
## iter 20 value 3625.834662
## iter 30 value 3079.268039
## iter 40 value 2932.355537
## iter 50 value 2418.294763
## iter 60 value 1667.722069
## iter 70 value 1509.250946
## iter 80 value 1332.558187
## iter 90 value 1286.847206
## iter 100 value 1269.895138
## iter 110 value 1251.510245
## iter 120 value 1244.060223
## iter 130 value 1218.629738
## iter 140 value 1196.282727
## iter 150 value 1149.970287
## iter 160 value 1099.627041
## iter 170 value 1066.795443
## iter 180 value 1016.979808
## iter 190 value 995.532014
## iter 200 value 989.616527
## iter 210 value 988.702919
## iter 220 value 983.951636
## iter 230 value 970.995146
## iter 240 value 963.502618
## iter 250 value 961.800353
## iter 260 value 961.319842
## iter 270 value 961.045437
## iter 280 value 961.036412
## iter 290 value 960.997069
## iter 300 value 960.892077
## iter 310 value 960.795150
## iter 320 value 960.756013
## iter 330 value 960.722899
## iter 340 value 960.566816
## iter 350 value 960.508370
## iter 360 value 960.501237
## iter 370 value 960.387318
## iter 380 value 959.168468
## iter 390 value 936.004814
## iter 400 value 929.486939
## iter 410 value 928.064978
## iter 420 value 926.698322
## iter 430 value 926.637411
## iter 440 value 926.497954
## iter 450 value 925.969762
## iter 460 value 925.702800
## iter 470 value 925.630139
## iter 480 value 925.621276
## final value 925.620160
## converged
## # weights: 71
## initial value 1448594.288173
## iter 10 value 1998.884294
## iter 20 value 1249.210458
## iter 30 value 1039.331207
## iter 40 value 949.907337
## iter 50 value 885.072117
## iter 60 value 836.653176
## iter 70 value 803.250524
## iter 80 value 772.197971
## iter 90 value 715.745609
## iter 100 value 682.632037
## iter 110 value 669.647726
## iter 120 value 658.561556
## iter 130 value 647.574728
## iter 140 value 633.092437
## iter 150 value 629.414717
## iter 160 value 628.385676
## iter 170 value 626.133596
## iter 180 value 619.577671
## iter 190 value 614.019318
## iter 200 value 606.773262
## iter 210 value 591.076212
## iter 220 value 579.598162
## iter 230 value 573.723423
## iter 240 value 565.237458
## iter 250 value 562.430163
## iter 260 value 560.967748
## iter 270 value 560.312162
## iter 280 value 559.792420
## iter 290 value 559.499271
## iter 300 value 559.482463
## iter 310 value 559.434718
## iter 320 value 559.343581
## iter 330 value 559.220237
## iter 340 value 559.174525
## iter 350 value 559.080966
## iter 360 value 559.064228
## iter 370 value 559.060605
## final value 559.060347
## converged
## # weights: 106
## initial value 1371057.175926
## iter 10 value 1603.382123
## iter 20 value 1100.374844
## iter 30 value 925.027665
## iter 40 value 745.631744
## iter 50 value 655.625838
## iter 60 value 589.808411
## iter 70 value 556.493895
## iter 80 value 537.184875
## iter 90 value 522.169353
## iter 100 value 509.229286
## iter 110 value 499.542885
## iter 120 value 492.351116
## iter 130 value 481.882074
## iter 140 value 466.555841
## iter 150 value 452.173222
## iter 160 value 438.563390
## iter 170 value 429.011152
## iter 180 value 423.009685
## iter 190 value 411.752831
## iter 200 value 400.183627
## iter 210 value 393.507508
## iter 220 value 390.993711
## iter 230 value 388.846996
## iter 240 value 384.655022
## iter 250 value 380.523247
## iter 260 value 373.929378
## iter 270 value 366.448851
## iter 280 value 361.663323
## iter 290 value 357.166943
## iter 300 value 350.395028
## iter 310 value 344.855082
## iter 320 value 338.122554
## iter 330 value 331.705616
## iter 340 value 325.879371
## iter 350 value 320.352971
## iter 360 value 316.539782
## iter 370 value 313.477916
## iter 380 value 311.284552
## iter 390 value 309.131812
## iter 400 value 305.835875
## iter 410 value 302.043903
## iter 420 value 298.777507
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## iter 450 value 296.678795
## iter 460 value 295.993659
## iter 470 value 295.440906
## iter 480 value 294.851147
## iter 490 value 294.474237
## iter 500 value 294.139842
## final value 294.139842
## stopped after 500 iterations
## # weights: 141
## initial value 1433748.111499
## iter 10 value 1716.685914
## iter 20 value 1126.974194
## iter 30 value 848.670411
## iter 40 value 731.991249
## iter 50 value 647.619439
## iter 60 value 554.629487
## iter 70 value 514.718376
## iter 80 value 473.565282
## iter 90 value 445.613081
## iter 100 value 420.690106
## iter 110 value 393.139626
## iter 120 value 373.615705
## iter 130 value 346.903443
## iter 140 value 323.140093
## iter 150 value 300.935461
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## iter 180 value 261.954418
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## iter 200 value 246.904057
## iter 210 value 236.881883
## iter 220 value 230.048595
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## iter 240 value 224.463995
## iter 250 value 221.362550
## iter 260 value 216.849894
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## iter 290 value 212.131582
## iter 300 value 211.864822
## iter 310 value 211.321963
## iter 320 value 210.552533
## iter 330 value 209.330324
## iter 340 value 207.089743
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## iter 360 value 200.154507
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## iter 380 value 194.735735
## iter 390 value 193.101472
## iter 400 value 191.900433
## iter 410 value 189.408658
## iter 420 value 185.753655
## iter 430 value 182.121570
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## iter 450 value 178.817384
## iter 460 value 177.556732
## iter 470 value 176.637584
## iter 480 value 176.057998
## iter 490 value 175.813406
## iter 500 value 175.678030
## final value 175.678030
## stopped after 500 iterations
## # weights: 15
## initial value 1414275.057729
## iter 10 value 15993.717911
## iter 20 value 14452.202363
## iter 30 value 7909.059426
## iter 40 value 4864.348001
## iter 50 value 2074.802229
## iter 60 value 1869.589327
## iter 70 value 1854.122154
## iter 80 value 1773.932000
## iter 90 value 1693.198275
## iter 100 value 1652.898882
## iter 110 value 1646.097513
## iter 120 value 1630.981011
## iter 130 value 1624.764947
## iter 140 value 1619.108699
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## iter 160 value 1614.566990
## iter 170 value 1612.545623
## iter 180 value 1612.505240
## iter 190 value 1608.285391
## iter 200 value 1598.591962
## iter 210 value 1504.491139
## iter 220 value 1473.929736
## iter 230 value 1466.841804
## iter 240 value 1457.972653
## iter 250 value 1456.437960
## iter 260 value 1456.286939
## iter 270 value 1455.189793
## iter 280 value 1455.049900
## iter 290 value 1455.028425
## iter 300 value 1454.814431
## iter 310 value 1454.605030
## iter 320 value 1454.583014
## iter 330 value 1454.282042
## iter 340 value 1454.258043
## iter 350 value 1454.252589
## iter 360 value 1454.237810
## iter 370 value 1454.202741
## final value 1454.195031
## converged
## # weights: 36
## initial value 1402841.127600
## iter 10 value 228827.546118
## iter 20 value 10211.092963
## iter 30 value 6251.151183
## iter 40 value 5867.695134
## iter 50 value 5609.435087
## iter 60 value 5548.416626
## iter 70 value 5546.900389
## iter 80 value 5438.673466
## iter 90 value 5074.979683
## iter 100 value 4484.504870
## iter 110 value 4073.955045
## iter 120 value 3408.894762
## iter 130 value 2776.611584
## iter 140 value 2257.032629
## iter 150 value 1869.538054
## iter 160 value 1777.757859
## iter 170 value 1715.453915
## iter 180 value 1664.946109
## iter 190 value 1657.823633
## iter 200 value 1565.906107
## iter 210 value 1523.844259
## iter 220 value 1504.158555
## iter 230 value 1500.596163
## iter 240 value 1494.814806
## iter 250 value 1483.980230
## iter 260 value 1479.722417
## iter 270 value 1470.198098
## iter 280 value 1468.757975
## iter 290 value 1463.498356
## iter 300 value 1461.558397
## iter 310 value 1460.552166
## iter 320 value 1460.280731
## iter 330 value 1459.225552
## iter 340 value 1458.314760
## iter 350 value 1457.799016
## iter 360 value 1437.935802
## iter 370 value 1422.248602
## iter 380 value 1403.731008
## iter 390 value 1382.191780
## iter 400 value 1381.367892
## iter 410 value 1379.709743
## iter 420 value 1376.487023
## iter 430 value 1354.741249
## iter 440 value 1345.648898
## iter 450 value 1345.430448
## iter 460 value 1345.325696
## iter 470 value 1344.149590
## iter 480 value 1343.522160
## iter 490 value 1342.892640
## iter 500 value 1341.942434
## final value 1341.942434
## stopped after 500 iterations
## # weights: 71
## initial value 1449975.565530
## iter 10 value 40444.399758
## iter 20 value 6075.795049
## iter 30 value 3862.815550
## iter 40 value 3083.978612
## iter 50 value 2662.046461
## iter 60 value 2322.254135
## iter 70 value 1954.854662
## iter 80 value 1583.901698
## iter 90 value 1444.266789
## iter 100 value 1398.010418
## iter 110 value 1372.933580
## iter 120 value 1351.217501
## iter 130 value 1317.944425
## iter 140 value 1276.713502
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## iter 160 value 1242.391358
## iter 170 value 1157.246799
## iter 180 value 1057.847438
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## iter 200 value 996.808407
## iter 210 value 967.564529
## iter 220 value 939.575163
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## iter 250 value 888.570445
## iter 260 value 882.932774
## iter 270 value 880.446083
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## iter 300 value 878.612049
## iter 310 value 876.233187
## iter 320 value 874.387299
## iter 330 value 871.969478
## iter 340 value 868.290497
## iter 350 value 865.042164
## iter 360 value 864.015139
## iter 370 value 863.599565
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## iter 400 value 862.974959
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## iter 420 value 860.171656
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## iter 470 value 823.829291
## iter 480 value 822.432562
## iter 490 value 821.975579
## iter 500 value 821.735226
## final value 821.735226
## stopped after 500 iterations
## # weights: 106
## initial value 1336980.291867
## iter 10 value 1382.243435
## iter 20 value 1128.860289
## iter 30 value 1021.675856
## iter 40 value 914.316224
## iter 50 value 837.577559
## iter 60 value 763.040455
## iter 70 value 683.074378
## iter 80 value 621.558741
## iter 90 value 592.998099
## iter 100 value 533.299534
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## iter 190 value 397.428769
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## iter 300 value 359.930813
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## iter 480 value 345.748892
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## iter 500 value 345.737004
## final value 345.737004
## stopped after 500 iterations
## # weights: 141
## initial value 1413241.499437
## iter 10 value 2279.795097
## iter 20 value 1175.898175
## iter 30 value 976.188393
## iter 40 value 811.499283
## iter 50 value 695.977927
## iter 60 value 640.718240
## iter 70 value 585.698489
## iter 80 value 533.511471
## iter 90 value 490.506825
## iter 100 value 458.070088
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## iter 120 value 386.702555
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## iter 160 value 290.376928
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## iter 190 value 266.262022
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## iter 220 value 242.318429
## iter 230 value 235.534123
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## iter 420 value 188.672407
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## iter 450 value 181.023997
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## iter 470 value 179.673985
## iter 480 value 178.550208
## iter 490 value 177.661683
## iter 500 value 177.302370
## final value 177.302370
## stopped after 500 iterations
## # weights: 15
## initial value 1398267.615428
## iter 10 value 11619.407758
## iter 20 value 5352.990587
## iter 30 value 4647.506039
## iter 40 value 4461.688077
## iter 50 value 4432.359454
## iter 60 value 4408.846050
## iter 70 value 3999.030467
## iter 80 value 3908.154365
## iter 90 value 3897.943932
## iter 100 value 3892.703329
## iter 110 value 3892.294242
## iter 120 value 3886.270935
## iter 130 value 3870.236258
## iter 140 value 3864.745158
## iter 150 value 3818.714913
## iter 160 value 3758.256686
## iter 170 value 3734.727130
## iter 180 value 3711.406103
## iter 190 value 3215.751492
## iter 200 value 2259.044848
## iter 210 value 1709.045601
## iter 220 value 1645.635927
## iter 230 value 1592.875361
## iter 240 value 1564.872231
## iter 250 value 1551.023979
## iter 260 value 1547.415262
## iter 270 value 1539.517998
## iter 280 value 1537.306871
## iter 290 value 1536.106282
## iter 300 value 1533.038290
## iter 310 value 1531.696399
## iter 320 value 1531.654871
## iter 330 value 1529.604686
## iter 340 value 1528.612561
## iter 350 value 1528.599485
## iter 360 value 1528.008299
## iter 370 value 1527.704097
## final value 1527.704063
## converged
## # weights: 36
## initial value 1425884.984400
## iter 10 value 4274.641900
## iter 20 value 3328.818414
## iter 30 value 2467.323987
## iter 40 value 1882.492559
## iter 50 value 1753.893489
## iter 60 value 1724.666896
## iter 70 value 1664.142208
## iter 80 value 1557.235427
## iter 90 value 1454.255077
## iter 100 value 1379.264502
## iter 110 value 1320.661067
## iter 120 value 1279.833239
## iter 130 value 1255.141347
## iter 140 value 1248.975435
## iter 150 value 1247.564827
## iter 160 value 1240.850835
## iter 170 value 1236.421566
## iter 180 value 1234.224291
## iter 190 value 1232.937590
## iter 200 value 1231.782910
## iter 210 value 1231.164421
## iter 220 value 1231.143689
## iter 230 value 1230.912328
## iter 240 value 1230.587594
## iter 250 value 1230.338139
## iter 260 value 1230.121232
## iter 270 value 1229.762269
## iter 280 value 1229.483677
## iter 290 value 1229.281246
## iter 300 value 1229.144030
## iter 310 value 1229.014965
## iter 320 value 1228.977646
## iter 330 value 1228.805885
## iter 340 value 1228.563171
## iter 350 value 1228.545104
## final value 1228.540649
## converged
## # weights: 71
## initial value 1400456.740564
## iter 10 value 3769.503148
## iter 20 value 1609.621536
## iter 30 value 1132.550349
## iter 40 value 961.110155
## iter 50 value 917.997930
## iter 60 value 862.524135
## iter 70 value 809.792333
## iter 80 value 780.232878
## iter 90 value 754.344792
## iter 100 value 738.271386
## iter 110 value 717.508006
## iter 120 value 695.799373
## iter 130 value 674.496613
## iter 140 value 630.817995
## iter 150 value 591.225520
## iter 160 value 576.693149
## iter 170 value 567.232258
## iter 180 value 565.322388
## iter 190 value 564.212179
## iter 200 value 563.463263
## iter 210 value 562.087923
## iter 220 value 561.191284
## iter 230 value 560.446339
## iter 240 value 560.171669
## iter 250 value 559.893370
## iter 260 value 559.576179
## iter 270 value 559.265503
## iter 280 value 558.987343
## iter 290 value 558.599411
## iter 300 value 557.590485
## iter 310 value 557.261948
## iter 320 value 556.330804
## iter 330 value 555.574914
## iter 340 value 555.550285
## iter 350 value 555.535896
## iter 360 value 555.505884
## final value 555.482162
## converged
## # weights: 106
## initial value 1336939.781029
## iter 10 value 1754.972796
## iter 20 value 1094.500757
## iter 30 value 936.752908
## iter 40 value 834.680610
## iter 50 value 750.982570
## iter 60 value 700.301408
## iter 70 value 651.567954
## iter 80 value 626.308397
## iter 90 value 585.727432
## iter 100 value 548.558212
## iter 110 value 516.426520
## iter 120 value 491.223989
## iter 130 value 479.461800
## iter 140 value 466.830406
## iter 150 value 450.233048
## iter 160 value 439.074105
## iter 170 value 429.181798
## iter 180 value 410.458859
## iter 190 value 401.622316
## iter 200 value 395.574096
## iter 210 value 391.355808
## iter 220 value 387.697246
## iter 230 value 386.481693
## iter 240 value 384.761524
## iter 250 value 381.761226
## iter 260 value 374.295363
## iter 270 value 367.077224
## iter 280 value 361.753023
## iter 290 value 355.441133
## iter 300 value 345.515320
## iter 310 value 339.600873
## iter 320 value 335.162353
## iter 330 value 333.390948
## iter 340 value 331.884928
## iter 350 value 330.990934
## iter 360 value 330.775421
## iter 370 value 330.127361
## iter 380 value 329.441873
## iter 390 value 329.128377
## iter 400 value 328.835852
## iter 410 value 328.569258
## iter 420 value 328.534451
## iter 430 value 328.390357
## iter 440 value 328.262871
## iter 450 value 328.168906
## iter 460 value 328.053485
## iter 470 value 328.010757
## iter 480 value 327.980793
## iter 490 value 327.918547
## iter 500 value 327.753972
## final value 327.753972
## stopped after 500 iterations
## # weights: 141
## initial value 1374961.131929
## iter 10 value 1831.333931
## iter 20 value 1100.759513
## iter 30 value 850.947399
## iter 40 value 734.750064
## iter 50 value 623.429004
## iter 60 value 572.232199
## iter 70 value 527.814731
## iter 80 value 492.226171
## iter 90 value 467.169913
## iter 100 value 442.971125
## iter 110 value 408.700256
## iter 120 value 376.701035
## iter 130 value 345.284574
## iter 140 value 322.484092
## iter 150 value 304.947307
## iter 160 value 289.332505
## iter 170 value 274.862248
## iter 180 value 266.611418
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## iter 200 value 248.400356
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## iter 260 value 216.766306
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## iter 300 value 208.443785
## iter 310 value 207.661347
## iter 320 value 206.788811
## iter 330 value 205.520869
## iter 340 value 204.476159
## iter 350 value 203.697017
## iter 360 value 201.833853
## iter 370 value 197.870000
## iter 380 value 195.701197
## iter 390 value 194.809276
## iter 400 value 194.326362
## iter 410 value 192.675713
## iter 420 value 189.786198
## iter 430 value 187.513462
## iter 440 value 186.848410
## iter 450 value 186.559953
## iter 460 value 186.393485
## iter 470 value 186.305587
## iter 480 value 185.760986
## iter 490 value 184.894006
## iter 500 value 184.529925
## final value 184.529925
## stopped after 500 iterations
## # weights: 15
## initial value 1424983.074507
## iter 10 value 3184.335134
## iter 20 value 1856.914509
## iter 30 value 1725.120665
## iter 40 value 1689.391178
## iter 50 value 1623.926095
## iter 60 value 1522.283189
## iter 70 value 1516.321418
## iter 80 value 1479.225989
## iter 90 value 1448.437455
## iter 100 value 1446.477076
## iter 110 value 1437.599280
## iter 120 value 1429.186688
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## iter 320 value 1330.577360
## iter 330 value 1329.367964
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## iter 350 value 1328.732937
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## iter 380 value 1327.664792
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## iter 400 value 1327.017540
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## iter 420 value 1326.426259
## iter 430 value 1325.969052
## iter 440 value 1325.963325
## iter 450 value 1325.736175
## iter 460 value 1325.399859
## iter 470 value 1325.388595
## iter 480 value 1325.279220
## iter 490 value 1324.516932
## iter 500 value 1324.462657
## final value 1324.462657
## stopped after 500 iterations
## # weights: 36
## initial value 1421277.795807
## iter 10 value 15328.252597
## iter 20 value 6906.319879
## iter 30 value 3875.178882
## iter 40 value 1812.569604
## iter 50 value 1321.756770
## iter 60 value 1136.043716
## iter 70 value 1032.106810
## iter 80 value 1019.772689
## iter 90 value 1015.064454
## iter 100 value 995.723278
## iter 110 value 981.964414
## iter 120 value 973.206297
## iter 130 value 967.615874
## iter 140 value 963.991324
## iter 150 value 962.004563
## iter 160 value 961.965358
## iter 170 value 961.634283
## iter 180 value 961.358432
## iter 190 value 961.129678
## iter 200 value 960.241016
## iter 210 value 958.877263
## iter 220 value 957.420689
## iter 230 value 957.398436
## iter 240 value 957.360362
## iter 250 value 957.269326
## iter 260 value 957.204316
## iter 270 value 956.987902
## iter 280 value 956.829697
## iter 290 value 954.517657
## iter 300 value 954.037585
## iter 310 value 953.946375
## iter 320 value 953.563411
## iter 330 value 953.352571
## iter 340 value 953.318728
## iter 350 value 952.635606
## iter 360 value 952.249214
## iter 370 value 951.955832
## iter 380 value 951.943652
## iter 390 value 951.917819
## iter 400 value 951.819838
## iter 410 value 951.793436
## iter 420 value 951.721970
## iter 430 value 951.611241
## iter 440 value 951.548690
## iter 450 value 951.546125
## iter 450 value 951.546118
## iter 450 value 951.546118
## final value 951.546118
## converged
## # weights: 71
## initial value 1366521.387012
## iter 10 value 1350.201908
## iter 20 value 1016.016646
## iter 30 value 917.464908
## iter 40 value 870.256786
## iter 50 value 828.985060
## iter 60 value 799.745985
## iter 70 value 776.622199
## iter 80 value 751.315682
## iter 90 value 728.461335
## iter 100 value 698.342625
## iter 110 value 684.419145
## iter 120 value 676.521419
## iter 130 value 667.144859
## iter 140 value 661.673303
## iter 150 value 658.957310
## iter 160 value 655.500146
## iter 170 value 649.196530
## iter 180 value 643.154262
## iter 190 value 640.242539
## iter 200 value 635.569303
## iter 210 value 626.713164
## iter 220 value 612.017961
## iter 230 value 598.976373
## iter 240 value 588.704444
## iter 250 value 583.014381
## iter 260 value 577.745791
## iter 270 value 574.534050
## iter 280 value 573.606309
## iter 290 value 572.711124
## iter 300 value 572.486275
## iter 310 value 572.223319
## iter 320 value 572.122395
## iter 330 value 571.617871
## iter 340 value 569.738609
## iter 350 value 568.636824
## iter 360 value 568.270575
## iter 370 value 568.001508
## iter 380 value 567.830627
## iter 390 value 567.737005
## iter 400 value 567.647736
## iter 410 value 567.033922
## iter 420 value 559.613023
## iter 430 value 558.751537
## iter 440 value 558.702739
## iter 450 value 558.666222
## iter 460 value 558.609457
## iter 470 value 558.563013
## iter 480 value 558.447521
## iter 490 value 558.012973
## iter 500 value 557.754738
## final value 557.754738
## stopped after 500 iterations
## # weights: 106
## initial value 1412090.511358
## iter 10 value 1494.175724
## iter 20 value 1053.236614
## iter 30 value 926.095057
## iter 40 value 800.034680
## iter 50 value 740.946067
## iter 60 value 706.851837
## iter 70 value 673.778461
## iter 80 value 645.859827
## iter 90 value 599.567343
## iter 100 value 567.635545
## iter 110 value 542.638717
## iter 120 value 514.956111
## iter 130 value 495.128135
## iter 140 value 477.895883
## iter 150 value 460.157152
## iter 160 value 443.890560
## iter 170 value 428.705097
## iter 180 value 419.989287
## iter 190 value 414.454392
## iter 200 value 410.785414
## iter 210 value 406.183569
## iter 220 value 404.051716
## iter 230 value 402.095123
## iter 240 value 399.147165
## iter 250 value 395.577415
## iter 260 value 389.831574
## iter 270 value 385.342418
## iter 280 value 379.871588
## iter 290 value 373.750177
## iter 300 value 364.282784
## iter 310 value 356.193861
## iter 320 value 348.855263
## iter 330 value 342.435595
## iter 340 value 338.603993
## iter 350 value 336.808226
## iter 360 value 333.488791
## iter 370 value 329.659067
## iter 380 value 328.158702
## iter 390 value 326.845605
## iter 400 value 326.081922
## iter 410 value 325.660048
## iter 420 value 324.867680
## iter 430 value 324.446567
## iter 440 value 324.395327
## iter 450 value 324.309913
## iter 460 value 324.240130
## iter 470 value 324.131057
## iter 480 value 323.876405
## iter 490 value 323.477908
## iter 500 value 323.191407
## final value 323.191407
## stopped after 500 iterations
## # weights: 141
## initial value 1435279.203886
## iter 10 value 1863.870093
## iter 20 value 1006.341184
## iter 30 value 859.730613
## iter 40 value 735.984331
## iter 50 value 664.509105
## iter 60 value 612.967306
## iter 70 value 555.417067
## iter 80 value 506.920627
## iter 90 value 477.093636
## iter 100 value 459.829205
## iter 110 value 438.643502
## iter 120 value 405.989912
## iter 130 value 374.277064
## iter 140 value 341.918017
## iter 150 value 320.991637
## iter 160 value 304.919845
## iter 170 value 284.026379
## iter 180 value 271.731189
## iter 190 value 266.276569
## iter 200 value 256.866124
## iter 210 value 248.634888
## iter 220 value 240.094533
## iter 230 value 235.503319
## iter 240 value 231.726380
## iter 250 value 226.511500
## iter 260 value 219.410012
## iter 270 value 210.476832
## iter 280 value 202.949763
## iter 290 value 200.197208
## iter 300 value 198.200849
## iter 310 value 195.750135
## iter 320 value 193.207088
## iter 330 value 190.659507
## iter 340 value 188.951442
## iter 350 value 187.889466
## iter 360 value 186.311763
## iter 370 value 183.815835
## iter 380 value 180.897434
## iter 390 value 176.815870
## iter 400 value 173.410807
## iter 410 value 171.370605
## iter 420 value 168.742920
## iter 430 value 163.764221
## iter 440 value 159.835772
## iter 450 value 156.073842
## iter 460 value 153.724553
## iter 470 value 152.378150
## iter 480 value 151.592847
## iter 490 value 150.949857
## iter 500 value 150.180509
## final value 150.180509
## stopped after 500 iterations
## # weights: 15
## initial value 1421937.842268
## iter 10 value 9841.528333
## iter 20 value 6032.932905
## iter 30 value 5275.628985
## iter 40 value 4034.263567
## iter 50 value 2876.540130
## iter 60 value 2009.115668
## iter 70 value 1573.939919
## iter 80 value 1501.454923
## iter 90 value 1421.594909
## iter 100 value 1393.863434
## iter 110 value 1391.194916
## iter 120 value 1387.545279
## final value 1387.510043
## converged
## # weights: 36
## initial value 1418151.550676
## iter 10 value 51620.583755
## iter 20 value 9348.115332
## iter 30 value 6638.657634
## iter 40 value 3865.995839
## iter 50 value 2152.095467
## iter 60 value 1750.828106
## iter 70 value 1543.496365
## iter 80 value 1431.066410
## iter 90 value 1331.637340
## iter 100 value 1287.197586
## iter 110 value 1253.961821
## iter 120 value 1210.168873
## iter 130 value 1193.841768
## iter 140 value 1187.301892
## iter 150 value 1174.107301
## iter 160 value 1167.790235
## iter 170 value 1166.767672
## iter 180 value 1165.580066
## iter 190 value 1162.215981
## iter 200 value 1160.108590
## iter 210 value 1157.934453
## iter 220 value 1148.951332
## iter 230 value 1144.488777
## iter 240 value 1141.751269
## iter 250 value 1139.510403
## iter 260 value 1135.217482
## iter 270 value 1130.786250
## iter 280 value 1125.646633
## iter 290 value 1119.261157
## iter 300 value 1117.765979
## iter 310 value 1117.594749
## iter 320 value 1117.567856
## iter 330 value 1117.540571
## iter 340 value 1117.531894
## final value 1117.531173
## converged
## # weights: 71
## initial value 1367285.629532
## iter 10 value 4801.349716
## iter 20 value 3483.713084
## iter 30 value 3187.372938
## iter 40 value 2924.200310
## iter 50 value 2734.437484
## iter 60 value 2413.925923
## iter 70 value 2029.980447
## iter 80 value 1731.364718
## iter 90 value 1589.571834
## iter 100 value 1453.117213
## iter 110 value 1301.421105
## iter 120 value 1218.340091
## iter 130 value 1153.770736
## iter 140 value 1099.645962
## iter 150 value 1085.445809
## iter 160 value 1068.441741
## iter 170 value 1050.156267
## iter 180 value 1035.094906
## iter 190 value 1026.180947
## iter 200 value 1010.802956
## iter 210 value 999.352440
## iter 220 value 996.212885
## iter 230 value 986.084437
## iter 240 value 963.456190
## iter 250 value 945.103155
## iter 260 value 925.946221
## iter 270 value 909.731650
## iter 280 value 890.317120
## iter 290 value 882.789164
## iter 300 value 880.530894
## iter 310 value 878.775762
## iter 320 value 877.310864
## iter 330 value 876.421988
## iter 340 value 875.626201
## iter 350 value 853.830309
## iter 360 value 837.970615
## iter 370 value 832.484980
## iter 380 value 825.408212
## iter 390 value 820.984671
## iter 400 value 818.375632
## iter 410 value 813.446804
## iter 420 value 810.367794
## iter 430 value 809.730352
## iter 440 value 809.720850
## iter 450 value 809.708653
## iter 460 value 809.698730
## iter 470 value 809.695645
## iter 480 value 809.694909
## final value 809.694894
## converged
## # weights: 106
## initial value 1383754.248993
## iter 10 value 1442.611223
## iter 20 value 1074.695873
## iter 30 value 981.517233
## iter 40 value 933.462850
## iter 50 value 870.021077
## iter 60 value 813.550223
## iter 70 value 752.773095
## iter 80 value 717.092668
## iter 90 value 694.078603
## iter 100 value 676.380635
## iter 110 value 664.318065
## iter 120 value 655.471418
## iter 130 value 643.805697
## iter 140 value 638.775514
## iter 150 value 635.443607
## iter 160 value 633.323309
## iter 170 value 632.041302
## iter 180 value 630.902589
## iter 190 value 630.213338
## iter 200 value 629.539486
## iter 210 value 628.806289
## iter 220 value 628.632929
## iter 230 value 628.260194
## iter 240 value 627.541860
## iter 250 value 626.862333
## iter 260 value 626.241576
## iter 270 value 625.595962
## iter 280 value 625.207543
## iter 290 value 625.013305
## iter 300 value 624.854849
## iter 310 value 624.678406
## iter 320 value 624.597537
## iter 330 value 624.583958
## iter 340 value 624.562869
## iter 350 value 623.928459
## iter 360 value 622.410993
## iter 370 value 621.958179
## iter 380 value 621.611626
## iter 390 value 621.402205
## iter 400 value 621.370432
## iter 410 value 621.353866
## iter 420 value 621.349589
## final value 621.349126
## converged
## # weights: 141
## initial value 1362691.192027
## iter 10 value 1396.214450
## iter 20 value 1053.304048
## iter 30 value 970.142883
## iter 40 value 900.699605
## iter 50 value 855.719454
## iter 60 value 822.132898
## iter 70 value 791.549221
## iter 80 value 748.934954
## iter 90 value 718.493823
## iter 100 value 692.455716
## iter 110 value 672.134362
## iter 120 value 660.140616
## iter 130 value 653.144535
## iter 140 value 644.336068
## iter 150 value 632.411751
## iter 160 value 608.486967
## iter 170 value 585.289480
## iter 180 value 564.997697
## iter 190 value 557.686968
## iter 200 value 550.520342
## iter 210 value 545.267709
## iter 220 value 540.856281
## iter 230 value 538.662005
## iter 240 value 537.619865
## iter 250 value 536.843202
## iter 260 value 535.521756
## iter 270 value 534.366532
## iter 280 value 533.712529
## iter 290 value 533.256397
## iter 300 value 532.443802
## iter 310 value 530.825332
## iter 320 value 530.163911
## iter 330 value 529.882426
## iter 340 value 529.649631
## iter 350 value 529.438600
## iter 360 value 528.904547
## iter 370 value 527.564948
## iter 380 value 525.683209
## iter 390 value 524.802460
## iter 400 value 524.140795
## iter 410 value 521.752954
## iter 420 value 513.387863
## iter 430 value 510.366055
## iter 440 value 508.452656
## iter 450 value 508.061287
## iter 460 value 507.943478
## iter 470 value 507.838333
## iter 480 value 507.814377
## iter 490 value 507.813459
## final value 507.813435
## converged
## # weights: 15
## initial value 1426595.928637
## iter 10 value 17017.878547
## iter 20 value 9090.032534
## iter 30 value 7990.107122
## iter 40 value 3925.163419
## iter 50 value 2626.191477
## iter 60 value 1916.954235
## iter 70 value 1707.395862
## iter 80 value 1663.693129
## iter 90 value 1625.374557
## iter 100 value 1612.579832
## iter 110 value 1604.971906
## iter 120 value 1583.369547
## iter 130 value 1572.311308
## iter 140 value 1557.353843
## iter 150 value 1496.937542
## iter 160 value 1458.404851
## iter 170 value 1413.605562
## iter 180 value 1411.493109
## iter 190 value 1410.764512
## iter 200 value 1407.259326
## final value 1406.632528
## converged
## # weights: 36
## initial value 1418371.926877
## iter 10 value 18916.628757
## iter 20 value 14428.087708
## iter 30 value 8836.216035
## iter 40 value 6193.600532
## iter 50 value 4847.058241
## iter 60 value 2727.566199
## iter 70 value 1579.207455
## iter 80 value 1273.087030
## iter 90 value 1197.786573
## iter 100 value 1117.201581
## iter 110 value 1047.415165
## iter 120 value 1005.820174
## iter 130 value 987.439754
## iter 140 value 977.710448
## iter 150 value 971.741813
## iter 160 value 971.254451
## iter 170 value 968.825270
## iter 180 value 957.729166
## iter 190 value 954.491699
## iter 200 value 948.678479
## iter 210 value 945.305976
## iter 220 value 942.935557
## iter 230 value 942.672985
## iter 240 value 942.065239
## iter 250 value 940.850766
## iter 260 value 940.337084
## iter 270 value 939.787963
## iter 280 value 939.373664
## iter 290 value 938.209345
## iter 300 value 937.556138
## iter 310 value 937.070763
## iter 320 value 935.447503
## iter 330 value 929.205554
## iter 340 value 926.801094
## iter 350 value 925.060744
## iter 360 value 918.935071
## iter 370 value 915.602477
## iter 380 value 914.461503
## iter 390 value 910.673473
## iter 400 value 907.777216
## iter 410 value 905.679867
## iter 420 value 901.733043
## iter 430 value 900.424816
## iter 440 value 900.082796
## iter 450 value 900.073318
## iter 460 value 900.063941
## iter 470 value 900.018861
## iter 480 value 899.935805
## iter 490 value 899.919352
## final value 899.917438
## converged
## # weights: 71
## initial value 1365783.165322
## iter 10 value 1738.285767
## iter 20 value 1104.327619
## iter 30 value 1015.964016
## iter 40 value 951.741409
## iter 50 value 905.605703
## iter 60 value 863.722931
## iter 70 value 832.741110
## iter 80 value 797.180052
## iter 90 value 770.857187
## iter 100 value 754.168714
## iter 110 value 745.598271
## iter 120 value 729.551934
## iter 130 value 712.645514
## iter 140 value 702.237575
## iter 150 value 698.939193
## iter 160 value 696.619563
## iter 170 value 694.132101
## iter 180 value 690.023416
## iter 190 value 680.254605
## iter 200 value 659.659904
## iter 210 value 651.656002
## iter 220 value 648.377889
## iter 230 value 645.582393
## iter 240 value 642.540150
## iter 250 value 637.746077
## iter 260 value 634.558705
## iter 270 value 631.761105
## iter 280 value 629.477412
## iter 290 value 624.818697
## iter 300 value 623.458960
## iter 310 value 622.297201
## iter 320 value 620.900142
## iter 330 value 618.351214
## iter 340 value 614.775985
## iter 350 value 613.663686
## iter 360 value 606.214610
## iter 370 value 603.134759
## iter 380 value 598.219786
## iter 390 value 596.867761
## iter 400 value 596.753512
## iter 410 value 596.692617
## iter 420 value 596.681170
## iter 430 value 596.677448
## final value 596.677417
## converged
## # weights: 106
## initial value 1403925.397441
## iter 10 value 1158.820793
## iter 20 value 979.543542
## iter 30 value 869.469869
## iter 40 value 755.283825
## iter 50 value 689.990563
## iter 60 value 629.020080
## iter 70 value 551.568211
## iter 80 value 500.112668
## iter 90 value 464.391972
## iter 100 value 433.144624
## iter 110 value 407.061326
## iter 120 value 393.616337
## iter 130 value 386.575665
## iter 140 value 383.499837
## iter 150 value 379.150474
## iter 160 value 373.352734
## iter 170 value 369.839224
## iter 180 value 362.430979
## iter 190 value 358.079622
## iter 200 value 354.408505
## iter 210 value 349.313274
## iter 220 value 348.651988
## iter 230 value 348.137856
## iter 240 value 347.508687
## iter 250 value 346.585487
## iter 260 value 345.159527
## iter 270 value 343.842136
## iter 280 value 342.261962
## iter 290 value 337.157496
## iter 300 value 334.125030
## iter 310 value 330.622591
## iter 320 value 323.880588
## iter 330 value 320.028270
## iter 340 value 314.262211
## iter 350 value 311.993419
## iter 360 value 309.377885
## iter 370 value 305.523619
## iter 380 value 302.722307
## iter 390 value 302.345489
## iter 400 value 302.126253
## iter 410 value 302.006545
## iter 420 value 301.899817
## iter 430 value 301.807418
## iter 440 value 301.798523
## iter 450 value 301.782906
## iter 460 value 301.710917
## iter 470 value 301.539631
## iter 480 value 301.482744
## iter 490 value 301.467992
## iter 500 value 301.436826
## final value 301.436826
## stopped after 500 iterations
## # weights: 141
## initial value 1344021.555710
## iter 10 value 1410.599884
## iter 20 value 926.415518
## iter 30 value 813.455961
## iter 40 value 749.896152
## iter 50 value 659.731260
## iter 60 value 588.791864
## iter 70 value 543.294616
## iter 80 value 502.239566
## iter 90 value 460.619564
## iter 100 value 436.474295
## iter 110 value 414.571104
## iter 120 value 402.924335
## iter 130 value 388.478877
## iter 140 value 375.464065
## iter 150 value 358.204972
## iter 160 value 334.757560
## iter 170 value 309.829527
## iter 180 value 296.616766
## iter 190 value 287.413895
## iter 200 value 277.235181
## iter 210 value 263.464152
## iter 220 value 251.810705
## iter 230 value 244.993638
## iter 240 value 236.692107
## iter 250 value 231.480771
## iter 260 value 229.002418
## iter 270 value 226.954971
## iter 280 value 223.887127
## iter 290 value 222.682037
## iter 300 value 222.141484
## iter 310 value 221.513557
## iter 320 value 220.535741
## iter 330 value 219.745105
## iter 340 value 218.763501
## iter 350 value 217.383479
## iter 360 value 216.164420
## iter 370 value 214.455334
## iter 380 value 212.248506
## iter 390 value 210.390765
## iter 400 value 209.102367
## iter 410 value 208.324314
## iter 420 value 207.475027
## iter 430 value 206.985703
## iter 440 value 206.556325
## iter 450 value 206.044048
## iter 460 value 204.989411
## iter 470 value 203.886669
## iter 480 value 202.093646
## iter 490 value 198.507242
## iter 500 value 196.978472
## final value 196.978472
## stopped after 500 iterations
## # weights: 15
## initial value 1403916.590347
## iter 10 value 19631.327003
## iter 20 value 16658.481632
## iter 30 value 16357.930964
## iter 40 value 13489.768771
## iter 50 value 9235.516530
## iter 60 value 8847.106372
## iter 70 value 8675.084162
## iter 80 value 8675.023715
## iter 90 value 8674.837734
## iter 100 value 8666.193536
## iter 110 value 8346.851523
## iter 120 value 4204.635979
## iter 130 value 2839.711124
## iter 140 value 2155.922053
## iter 150 value 1792.889848
## iter 160 value 1513.329598
## iter 170 value 1414.277991
## iter 180 value 1403.415451
## iter 190 value 1392.581874
## iter 200 value 1381.641742
## iter 210 value 1379.197087
## iter 220 value 1379.000313
## iter 230 value 1376.017257
## iter 240 value 1375.433271
## final value 1375.430641
## converged
## # weights: 36
## initial value 1375655.799795
## iter 10 value 3675.595029
## iter 20 value 2812.122350
## iter 30 value 2095.996179
## iter 40 value 1496.622637
## iter 50 value 1230.828022
## iter 60 value 1055.970831
## iter 70 value 992.355653
## iter 80 value 964.616021
## iter 90 value 956.936073
## iter 100 value 956.420746
## iter 110 value 954.259685
## iter 120 value 936.403765
## iter 130 value 923.369730
## iter 140 value 897.956771
## iter 150 value 887.951429
## iter 160 value 877.847602
## iter 170 value 875.988514
## iter 180 value 875.008930
## iter 190 value 873.122649
## iter 200 value 870.607752
## iter 210 value 867.679898
## iter 220 value 864.595219
## iter 230 value 861.542821
## iter 240 value 860.353147
## iter 250 value 859.812511
## iter 260 value 858.301185
## iter 270 value 848.321841
## iter 280 value 844.811816
## iter 290 value 843.722455
## iter 300 value 836.885345
## iter 310 value 834.609278
## iter 320 value 829.204387
## iter 330 value 828.306526
## iter 340 value 821.917830
## iter 350 value 819.455267
## iter 360 value 814.099108
## iter 370 value 812.865546
## iter 380 value 812.593179
## iter 390 value 811.934537
## iter 400 value 811.205739
## iter 410 value 811.071141
## iter 420 value 810.037752
## final value 809.999876
## converged
## # weights: 71
## initial value 1364099.852105
## iter 10 value 1549.214879
## iter 20 value 1163.111642
## iter 30 value 984.281810
## iter 40 value 907.080865
## iter 50 value 818.234327
## iter 60 value 774.008498
## iter 70 value 755.853317
## iter 80 value 729.182097
## iter 90 value 714.981308
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## final value 604.824480
## stopped after 500 iterations
## # weights: 106
## initial value 1395136.924542
## iter 10 value 1416.602984
## iter 20 value 999.944528
## iter 30 value 874.796497
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## final value 288.680206
## stopped after 500 iterations
## # weights: 141
## initial value 1406772.662054
## iter 10 value 1714.478941
## iter 20 value 1036.595551
## iter 30 value 828.527235
## iter 40 value 737.638283
## iter 50 value 657.972671
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## iter 400 value 210.188187
## iter 410 value 206.622860
## iter 420 value 202.827905
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## iter 460 value 193.950529
## iter 470 value 192.737697
## iter 480 value 191.669596
## iter 490 value 190.516192
## iter 500 value 189.397834
## final value 189.397834
## stopped after 500 iterations
## # weights: 15
## initial value 1376674.377536
## iter 10 value 5621.843818
## iter 20 value 5104.331639
## iter 30 value 4853.081946
## iter 40 value 4205.320797
## iter 50 value 3733.986549
## iter 60 value 3707.050660
## iter 70 value 3699.653566
## iter 80 value 3696.468391
## iter 90 value 3501.961455
## iter 100 value 2624.958484
## iter 110 value 1821.000497
## iter 120 value 1668.395432
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## iter 140 value 1464.809582
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## iter 170 value 1433.152033
## iter 180 value 1429.959829
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## iter 200 value 1425.968961
## iter 210 value 1424.744834
## iter 220 value 1424.740764
## iter 220 value 1424.740753
## iter 220 value 1424.740746
## final value 1424.740746
## converged
## # weights: 36
## initial value 1382691.125324
## iter 10 value 9947.002188
## iter 20 value 5546.759036
## iter 30 value 3929.123987
## iter 40 value 2792.684034
## iter 50 value 2099.202778
## iter 60 value 1792.648877
## iter 70 value 1711.009906
## iter 80 value 1598.733324
## iter 90 value 1535.649846
## iter 100 value 1503.335612
## iter 110 value 1491.616080
## iter 120 value 1490.686889
## iter 130 value 1488.842807
## iter 140 value 1486.488297
## iter 150 value 1484.885798
## iter 160 value 1484.331113
## iter 170 value 1483.932454
## iter 180 value 1483.654989
## iter 190 value 1483.578155
## final value 1483.577780
## converged
## # weights: 71
## initial value 1430181.745343
## iter 10 value 1359.795027
## iter 20 value 1025.856086
## iter 30 value 906.949488
## iter 40 value 858.445484
## iter 50 value 821.349893
## iter 60 value 804.166791
## iter 70 value 786.964711
## iter 80 value 774.083679
## iter 90 value 759.624385
## iter 100 value 748.166369
## iter 110 value 731.474802
## iter 120 value 708.467501
## iter 130 value 692.361138
## iter 140 value 671.358734
## iter 150 value 647.852048
## iter 160 value 633.198745
## iter 170 value 622.539341
## iter 180 value 610.526348
## iter 190 value 587.406880
## iter 200 value 568.933425
## iter 210 value 557.365421
## iter 220 value 552.371952
## iter 230 value 545.880122
## iter 240 value 541.438773
## iter 250 value 537.443648
## iter 260 value 533.742008
## iter 270 value 529.072583
## iter 280 value 524.360143
## iter 290 value 520.624747
## iter 300 value 520.093739
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## iter 330 value 515.616384
## iter 340 value 514.733478
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## iter 360 value 508.831928
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## iter 380 value 495.850960
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## iter 400 value 495.272253
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## iter 470 value 474.834714
## iter 480 value 472.634940
## iter 490 value 472.133852
## iter 500 value 471.703563
## final value 471.703563
## stopped after 500 iterations
## # weights: 106
## initial value 1388346.535000
## iter 10 value 1878.003060
## iter 20 value 1036.019015
## iter 30 value 828.655095
## iter 40 value 722.873776
## iter 50 value 639.073632
## iter 60 value 576.470101
## iter 70 value 536.501313
## iter 80 value 502.909736
## iter 90 value 471.026188
## iter 100 value 452.279119
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## iter 130 value 391.067232
## iter 140 value 367.668051
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## iter 180 value 342.895854
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## iter 200 value 331.410359
## iter 210 value 327.821525
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## iter 300 value 298.926932
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## iter 320 value 292.231072
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## iter 470 value 283.880113
## iter 480 value 283.875202
## iter 490 value 283.872609
## iter 500 value 283.863770
## final value 283.863770
## stopped after 500 iterations
## # weights: 141
## initial value 1348136.615119
## iter 10 value 1348.404639
## iter 20 value 1036.739033
## iter 30 value 912.863847
## iter 40 value 811.962117
## iter 50 value 678.852316
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## iter 90 value 437.829197
## iter 100 value 408.624370
## iter 110 value 391.100221
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## iter 160 value 288.283947
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## iter 180 value 263.238701
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## iter 290 value 194.581800
## iter 300 value 193.827179
## iter 310 value 192.737067
## iter 320 value 191.844497
## iter 330 value 190.354508
## iter 340 value 188.709876
## iter 350 value 186.701189
## iter 360 value 184.898027
## iter 370 value 183.576098
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## iter 400 value 173.329196
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## iter 420 value 167.688026
## iter 430 value 165.320267
## iter 440 value 162.658250
## iter 450 value 160.712742
## iter 460 value 159.937916
## iter 470 value 159.337227
## iter 480 value 158.947436
## iter 490 value 157.987249
## iter 500 value 157.122143
## final value 157.122143
## stopped after 500 iterations
## # weights: 15
## initial value 1427661.626729
## iter 10 value 13793.275941
## iter 20 value 9502.033391
## iter 30 value 6797.213984
## iter 40 value 4628.285184
## iter 50 value 2349.210858
## iter 60 value 1766.809270
## iter 70 value 1765.220716
## iter 80 value 1757.092608
## iter 90 value 1754.417945
## iter 100 value 1753.521984
## iter 110 value 1752.527095
## iter 120 value 1751.923661
## iter 130 value 1751.858340
## iter 130 value 1751.858335
## final value 1751.858194
## converged
## # weights: 36
## initial value 1377628.891625
## iter 10 value 3470.726041
## iter 20 value 2442.297627
## iter 30 value 2244.466817
## iter 40 value 2037.517612
## iter 50 value 2012.806539
## iter 60 value 1995.958433
## iter 70 value 1747.585129
## iter 80 value 1536.446254
## iter 90 value 1401.129600
## iter 100 value 1369.280905
## iter 110 value 1339.565971
## iter 120 value 1263.680829
## iter 130 value 1226.559174
## iter 140 value 1197.998205
## iter 150 value 1178.277910
## iter 160 value 1099.061407
## iter 170 value 1056.115049
## iter 180 value 1030.073685
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## iter 210 value 1012.492388
## iter 220 value 1008.327586
## iter 230 value 1000.741985
## iter 240 value 992.335732
## iter 250 value 977.904853
## iter 260 value 971.722418
## iter 270 value 971.104288
## iter 280 value 968.396919
## iter 290 value 965.322713
## iter 300 value 940.668319
## iter 310 value 935.363706
## iter 320 value 933.921653
## iter 330 value 933.426040
## iter 340 value 932.297846
## iter 350 value 931.460782
## iter 360 value 931.426861
## iter 370 value 931.241459
## iter 380 value 930.841915
## iter 390 value 929.712963
## iter 400 value 927.864537
## iter 410 value 926.110200
## iter 420 value 922.295063
## iter 430 value 921.912459
## iter 440 value 921.830175
## iter 450 value 921.455080
## iter 460 value 921.227791
## iter 470 value 921.132481
## iter 480 value 920.601806
## iter 490 value 920.344186
## iter 500 value 920.122982
## final value 920.122982
## stopped after 500 iterations
## # weights: 71
## initial value 1441814.345653
## iter 10 value 8964.553894
## iter 20 value 2697.397961
## iter 30 value 1642.198570
## iter 40 value 1315.795553
## iter 50 value 1142.534114
## iter 60 value 1055.405087
## iter 70 value 1000.562003
## iter 80 value 964.549049
## iter 90 value 930.033377
## iter 100 value 910.566422
## iter 110 value 880.577192
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## iter 300 value 786.925715
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## iter 320 value 780.815440
## iter 330 value 768.760920
## iter 340 value 751.616856
## iter 350 value 747.376681
## iter 360 value 745.859932
## iter 370 value 744.337745
## iter 380 value 743.109826
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## iter 400 value 741.023646
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## iter 420 value 739.409954
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## iter 470 value 739.278146
## iter 480 value 739.267116
## iter 490 value 739.244156
## iter 500 value 739.171731
## final value 739.171731
## stopped after 500 iterations
## # weights: 106
## initial value 1434450.161944
## iter 10 value 1277.871972
## iter 20 value 1017.304972
## iter 30 value 892.734701
## iter 40 value 800.416988
## iter 50 value 729.309155
## iter 60 value 679.817039
## iter 70 value 619.490133
## iter 80 value 578.488873
## iter 90 value 551.223548
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## iter 140 value 467.021798
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## iter 300 value 321.529210
## iter 310 value 318.370870
## iter 320 value 314.113507
## iter 330 value 306.837927
## iter 340 value 299.641913
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## iter 380 value 291.184495
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## iter 400 value 290.666830
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## iter 430 value 290.354936
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## iter 470 value 290.332895
## iter 480 value 290.309380
## iter 490 value 290.227195
## iter 500 value 290.114493
## final value 290.114493
## stopped after 500 iterations
## # weights: 141
## initial value 1412825.912914
## iter 10 value 1622.287227
## iter 20 value 1027.175289
## iter 30 value 933.843782
## iter 40 value 816.994609
## iter 50 value 728.817589
## iter 60 value 668.545782
## iter 70 value 612.028895
## iter 80 value 561.048547
## iter 90 value 519.495084
## iter 100 value 483.047482
## iter 110 value 453.567890
## iter 120 value 422.274667
## iter 130 value 402.452536
## iter 140 value 363.881929
## iter 150 value 342.392514
## iter 160 value 323.615831
## iter 170 value 313.252920
## iter 180 value 300.711601
## iter 190 value 289.230853
## iter 200 value 279.521352
## iter 210 value 267.254278
## iter 220 value 262.369089
## iter 230 value 253.680932
## iter 240 value 246.146566
## iter 250 value 236.879230
## iter 260 value 227.189029
## iter 270 value 219.682763
## iter 280 value 212.605946
## iter 290 value 209.810365
## iter 300 value 207.865112
## iter 310 value 204.917534
## iter 320 value 202.974789
## iter 330 value 200.721545
## iter 340 value 198.647181
## iter 350 value 197.305454
## iter 360 value 195.876452
## iter 370 value 192.568266
## iter 380 value 190.440122
## iter 390 value 187.374904
## iter 400 value 183.581968
## iter 410 value 180.260881
## iter 420 value 177.493354
## iter 430 value 174.679135
## iter 440 value 173.046095
## iter 450 value 172.056058
## iter 460 value 171.588915
## iter 470 value 171.196504
## iter 480 value 170.993620
## iter 490 value 170.795823
## iter 500 value 170.050674
## final value 170.050674
## stopped after 500 iterations
## # weights: 15
## initial value 1408986.934906
## iter 10 value 20985.853377
## iter 20 value 18459.320081
## iter 30 value 14466.829187
## iter 40 value 10360.851669
## iter 50 value 6077.681780
## iter 60 value 3193.120858
## iter 70 value 2710.417361
## iter 80 value 2015.950262
## iter 90 value 1944.507555
## iter 100 value 1944.268906
## final value 1943.770161
## converged
## # weights: 36
## initial value 1424952.463072
## iter 10 value 5181.022819
## iter 20 value 2990.784576
## iter 30 value 2146.527377
## iter 40 value 1792.535196
## iter 50 value 1679.140131
## iter 60 value 1482.584543
## iter 70 value 1407.448429
## iter 80 value 1363.953319
## iter 90 value 1309.425001
## iter 100 value 1281.526493
## iter 110 value 1277.619300
## iter 120 value 1276.951650
## iter 130 value 1275.365347
## iter 140 value 1275.113500
## final value 1275.112288
## converged
## # weights: 71
## initial value 1429060.172372
## iter 10 value 1418.147438
## iter 20 value 1193.100841
## iter 30 value 1138.425928
## iter 40 value 1109.584556
## iter 50 value 1080.077597
## iter 60 value 1035.973241
## iter 70 value 974.134717
## iter 80 value 938.002297
## iter 90 value 918.991655
## iter 100 value 910.385064
## iter 110 value 897.278739
## iter 120 value 880.451020
## iter 130 value 871.325790
## iter 140 value 867.609328
## iter 150 value 866.531714
## iter 160 value 864.909898
## iter 170 value 862.806475
## iter 180 value 860.059381
## iter 190 value 852.411528
## iter 200 value 843.481627
## iter 210 value 839.922647
## iter 220 value 837.711992
## iter 230 value 831.224292
## iter 240 value 827.610699
## iter 250 value 821.150431
## iter 260 value 818.464343
## iter 270 value 815.260980
## iter 280 value 811.319243
## iter 290 value 805.509097
## iter 300 value 804.189739
## iter 310 value 802.524599
## iter 320 value 798.877375
## iter 330 value 794.785623
## iter 340 value 790.469847
## iter 350 value 787.312113
## iter 360 value 786.128768
## iter 370 value 785.684914
## iter 380 value 785.606827
## final value 785.602927
## converged
## # weights: 106
## initial value 1457393.662670
## iter 10 value 1557.199836
## iter 20 value 1134.818062
## iter 30 value 999.912513
## iter 40 value 951.481670
## iter 50 value 897.201569
## iter 60 value 869.302980
## iter 70 value 826.885783
## iter 80 value 802.474842
## iter 90 value 777.673778
## iter 100 value 760.187391
## iter 110 value 742.766858
## iter 120 value 727.423780
## iter 130 value 718.035738
## iter 140 value 702.102235
## iter 150 value 684.594146
## iter 160 value 677.945601
## iter 170 value 669.492198
## iter 180 value 664.894441
## iter 190 value 662.344934
## iter 200 value 660.965928
## iter 210 value 658.859020
## iter 220 value 657.831002
## iter 230 value 656.324306
## iter 240 value 653.945843
## iter 250 value 650.616812
## iter 260 value 647.373679
## iter 270 value 644.620580
## iter 280 value 642.318794
## iter 290 value 640.374353
## iter 300 value 637.514473
## iter 310 value 624.212510
## iter 320 value 617.611809
## iter 330 value 615.647337
## iter 340 value 614.290170
## iter 350 value 608.560098
## iter 360 value 597.891043
## iter 370 value 595.209494
## iter 380 value 593.827338
## iter 390 value 593.094177
## iter 400 value 592.987570
## iter 410 value 592.965640
## iter 420 value 592.960258
## final value 592.959782
## converged
## # weights: 141
## initial value 1400810.069917
## iter 10 value 1799.311970
## iter 20 value 1303.511605
## iter 30 value 1027.560078
## iter 40 value 949.153520
## iter 50 value 874.085266
## iter 60 value 844.898199
## iter 70 value 809.437515
## iter 80 value 765.205522
## iter 90 value 746.127091
## iter 100 value 730.707761
## iter 110 value 718.157413
## iter 120 value 704.947451
## iter 130 value 694.985050
## iter 140 value 689.156792
## iter 150 value 683.970761
## iter 160 value 679.695849
## iter 170 value 675.859216
## iter 180 value 668.104260
## iter 190 value 651.416306
## iter 200 value 637.134357
## iter 210 value 625.783503
## iter 220 value 616.704054
## iter 230 value 608.045919
## iter 240 value 600.718818
## iter 250 value 595.242235
## iter 260 value 592.704917
## iter 270 value 590.925946
## iter 280 value 587.539662
## iter 290 value 583.880277
## iter 300 value 581.413736
## iter 310 value 575.973352
## iter 320 value 573.297888
## iter 330 value 571.296281
## iter 340 value 570.311583
## iter 350 value 568.923493
## iter 360 value 568.118303
## iter 370 value 566.588831
## iter 380 value 561.634314
## iter 390 value 560.163042
## iter 400 value 558.877240
## iter 410 value 557.867369
## iter 420 value 556.428619
## iter 430 value 555.584055
## iter 440 value 555.071903
## iter 450 value 554.859104
## iter 460 value 554.696828
## iter 470 value 554.690647
## final value 554.690363
## converged
## # weights: 15
## initial value 1447346.466397
## iter 10 value 9299.894054
## iter 20 value 3516.575673
## iter 30 value 2092.860369
## iter 40 value 1858.505191
## iter 50 value 1770.138144
## iter 60 value 1740.290535
## iter 70 value 1710.183812
## iter 80 value 1527.970253
## iter 90 value 1493.693263
## iter 100 value 1292.222793
## iter 110 value 1196.664669
## iter 120 value 1186.691979
## iter 130 value 1180.942644
## iter 140 value 1179.700850
## iter 150 value 1178.903426
## iter 160 value 1178.038924
## iter 170 value 1176.034017
## iter 180 value 1175.117315
## iter 190 value 1174.476149
## iter 200 value 1174.196668
## iter 210 value 1174.193115
## final value 1174.192520
## converged
## # weights: 36
## initial value 1356986.625888
## iter 10 value 7074.598401
## iter 20 value 2459.361402
## iter 30 value 1731.668905
## iter 40 value 1193.694142
## iter 50 value 1037.538754
## iter 60 value 1004.325158
## iter 70 value 994.277430
## iter 80 value 990.472406
## iter 90 value 988.878079
## iter 100 value 982.039727
## iter 110 value 970.323716
## iter 120 value 955.247332
## iter 130 value 944.909823
## iter 140 value 931.231258
## iter 150 value 912.187056
## iter 160 value 901.647812
## iter 170 value 895.922190
## iter 180 value 889.055226
## iter 190 value 878.372436
## iter 200 value 873.199637
## iter 210 value 870.405637
## iter 220 value 869.984116
## iter 230 value 869.918142
## iter 240 value 869.824494
## iter 250 value 869.746318
## iter 260 value 869.403499
## iter 270 value 869.397973
## final value 869.397452
## converged
## # weights: 71
## initial value 1417800.901559
## iter 10 value 1740.270332
## iter 20 value 1158.517677
## iter 30 value 1023.205752
## iter 40 value 926.083079
## iter 50 value 868.726272
## iter 60 value 825.349074
## iter 70 value 810.341172
## iter 80 value 751.334259
## iter 90 value 730.042979
## iter 100 value 697.275690
## iter 110 value 675.242850
## iter 120 value 656.822588
## iter 130 value 642.764232
## iter 140 value 622.291473
## iter 150 value 602.874378
## iter 160 value 592.151162
## iter 170 value 578.676084
## iter 180 value 561.857170
## iter 190 value 544.419308
## iter 200 value 534.891970
## iter 210 value 526.091616
## iter 220 value 517.149032
## iter 230 value 506.397388
## iter 240 value 502.883014
## iter 250 value 500.957554
## iter 260 value 498.713438
## iter 270 value 496.422961
## iter 280 value 494.281133
## iter 290 value 493.367401
## iter 300 value 493.171700
## iter 310 value 492.897882
## iter 320 value 492.590265
## iter 330 value 492.083835
## iter 340 value 490.771951
## iter 350 value 489.543882
## iter 360 value 489.238281
## iter 370 value 488.988331
## iter 380 value 488.937489
## iter 390 value 488.899004
## iter 400 value 488.888576
## iter 410 value 488.885926
## iter 420 value 488.885692
## final value 488.885685
## converged
## # weights: 106
## initial value 1374480.931723
## iter 10 value 1207.326961
## iter 20 value 1007.469962
## iter 30 value 895.488828
## iter 40 value 815.740049
## iter 50 value 752.202782
## iter 60 value 713.940912
## iter 70 value 662.738635
## iter 80 value 634.618804
## iter 90 value 614.331922
## iter 100 value 595.287286
## iter 110 value 578.044096
## iter 120 value 558.157726
## iter 130 value 541.312234
## iter 140 value 521.811302
## iter 150 value 499.493602
## iter 160 value 479.683491
## iter 170 value 464.004036
## iter 180 value 454.180065
## iter 190 value 446.839690
## iter 200 value 438.171123
## iter 210 value 423.998131
## iter 220 value 418.390025
## iter 230 value 415.771044
## iter 240 value 411.617170
## iter 250 value 403.315964
## iter 260 value 395.170007
## iter 270 value 388.060051
## iter 280 value 383.297416
## iter 290 value 378.403069
## iter 300 value 374.275601
## iter 310 value 370.498740
## iter 320 value 367.213614
## iter 330 value 366.268632
## iter 340 value 365.356791
## iter 350 value 365.140489
## iter 360 value 365.096208
## iter 370 value 365.086513
## iter 380 value 365.085364
## iter 390 value 365.085101
## final value 365.085093
## converged
## # weights: 141
## initial value 1457144.428539
## iter 10 value 1357.938142
## iter 20 value 1056.045132
## iter 30 value 925.175302
## iter 40 value 812.813513
## iter 50 value 750.331680
## iter 60 value 707.926008
## iter 70 value 653.277319
## iter 80 value 582.687107
## iter 90 value 536.159823
## iter 100 value 487.922223
## iter 110 value 458.811011
## iter 120 value 425.335333
## iter 130 value 388.791907
## iter 140 value 338.847453
## iter 150 value 320.730838
## iter 160 value 312.505654
## iter 170 value 298.349396
## iter 180 value 291.572209
## iter 190 value 280.357549
## iter 200 value 266.562766
## iter 210 value 254.535847
## iter 220 value 244.178157
## iter 230 value 237.643888
## iter 240 value 231.857390
## iter 250 value 224.345362
## iter 260 value 220.689994
## iter 270 value 218.606895
## iter 280 value 217.072419
## iter 290 value 216.532138
## iter 300 value 215.999574
## iter 310 value 215.285988
## iter 320 value 214.318708
## iter 330 value 212.310094
## iter 340 value 210.234280
## iter 350 value 208.193171
## iter 360 value 207.168231
## iter 370 value 206.143178
## iter 380 value 205.280567
## iter 390 value 204.495536
## iter 400 value 203.580698
## iter 410 value 202.896600
## iter 420 value 202.034853
## iter 430 value 200.754908
## iter 440 value 199.913948
## iter 450 value 199.467116
## iter 460 value 199.206448
## iter 470 value 198.699061
## iter 480 value 194.732367
## iter 490 value 191.657804
## iter 500 value 189.040110
## final value 189.040110
## stopped after 500 iterations
## # weights: 15
## initial value 1420233.231794
## iter 10 value 72824.933060
## iter 20 value 7686.083658
## iter 30 value 2585.826051
## iter 40 value 1889.052754
## iter 50 value 1818.810222
## iter 60 value 1769.759746
## iter 70 value 1759.660572
## iter 80 value 1758.303113
## iter 90 value 1754.949232
## iter 100 value 1754.033414
## iter 110 value 1749.794866
## iter 120 value 1748.930384
## iter 130 value 1747.485461
## iter 140 value 1741.655083
## iter 150 value 1737.890747
## iter 160 value 1737.380249
## iter 170 value 1734.990023
## iter 180 value 1730.467206
## iter 190 value 1701.748252
## iter 200 value 1657.638733
## iter 210 value 1611.231712
## iter 220 value 1347.596525
## iter 230 value 1247.001076
## iter 240 value 1220.440842
## iter 250 value 1187.254709
## iter 260 value 1180.289545
## iter 270 value 1180.142354
## iter 280 value 1179.820896
## iter 290 value 1179.351309
## iter 300 value 1179.318396
## iter 310 value 1179.104688
## iter 320 value 1178.443368
## iter 330 value 1178.217280
## iter 340 value 1177.976718
## iter 350 value 1177.483910
## iter 360 value 1177.145903
## iter 370 value 1176.872470
## iter 380 value 1175.771875
## iter 390 value 1174.936143
## iter 400 value 1174.020837
## iter 410 value 1172.296616
## iter 420 value 1171.803909
## iter 430 value 1171.710385
## iter 440 value 1171.630748
## final value 1171.626535
## converged
## # weights: 36
## initial value 1377478.536983
## iter 10 value 8245.875436
## iter 20 value 3072.328775
## iter 30 value 1894.546374
## iter 40 value 1383.894150
## iter 50 value 1149.882290
## iter 60 value 1086.575855
## iter 70 value 1041.940879
## iter 80 value 1037.713885
## iter 90 value 1028.718193
## iter 100 value 1013.551115
## iter 110 value 1000.828098
## iter 120 value 968.192258
## iter 130 value 948.528103
## iter 140 value 932.576946
## iter 150 value 913.320604
## iter 160 value 905.395123
## iter 170 value 898.727362
## iter 180 value 889.982531
## iter 190 value 886.226974
## iter 200 value 884.740083
## iter 210 value 883.939910
## iter 220 value 883.236821
## iter 230 value 883.144026
## iter 240 value 883.072724
## iter 250 value 882.818938
## iter 260 value 882.051527
## iter 270 value 881.412403
## iter 280 value 880.500545
## iter 290 value 879.603864
## iter 300 value 879.425685
## iter 310 value 879.402045
## iter 320 value 879.365995
## iter 330 value 879.335461
## iter 340 value 879.318525
## iter 350 value 879.267244
## iter 360 value 879.262811
## iter 370 value 879.218831
## iter 380 value 879.217606
## iter 390 value 879.212485
## iter 400 value 879.093072
## iter 410 value 879.062739
## iter 420 value 878.453998
## iter 430 value 876.135221
## iter 440 value 868.179758
## iter 450 value 862.467991
## iter 460 value 860.631476
## iter 470 value 859.901115
## iter 480 value 859.894583
## iter 490 value 859.882043
## iter 490 value 859.882041
## final value 859.882041
## converged
## # weights: 71
## initial value 1414148.933316
## iter 10 value 1639.009149
## iter 20 value 1119.523925
## iter 30 value 1018.934173
## iter 40 value 943.330056
## iter 50 value 898.298762
## iter 60 value 875.962676
## iter 70 value 852.757234
## iter 80 value 822.678789
## iter 90 value 786.678396
## iter 100 value 754.872652
## iter 110 value 726.064866
## iter 120 value 711.487305
## iter 130 value 696.580867
## iter 140 value 680.561011
## iter 150 value 671.881421
## iter 160 value 667.892477
## iter 170 value 659.283581
## iter 180 value 650.022477
## iter 190 value 641.495508
## iter 200 value 631.701145
## iter 210 value 607.242805
## iter 220 value 579.029365
## iter 230 value 567.176955
## iter 240 value 559.866760
## iter 250 value 556.780081
## iter 260 value 555.669176
## iter 270 value 554.578424
## iter 280 value 553.665928
## iter 290 value 553.381924
## iter 300 value 553.343726
## iter 310 value 553.298412
## iter 320 value 553.268264
## iter 330 value 553.192785
## iter 340 value 553.129840
## iter 350 value 552.115042
## iter 360 value 552.036909
## iter 370 value 550.997426
## iter 380 value 550.801109
## iter 390 value 550.775383
## final value 550.735265
## converged
## # weights: 106
## initial value 1453336.037855
## iter 10 value 1820.586956
## iter 20 value 1078.222267
## iter 30 value 940.487328
## iter 40 value 833.753838
## iter 50 value 749.525527
## iter 60 value 697.302406
## iter 70 value 664.434318
## iter 80 value 640.350711
## iter 90 value 623.931378
## iter 100 value 608.760205
## iter 110 value 591.108640
## iter 120 value 551.379667
## iter 130 value 512.613586
## iter 140 value 475.005600
## iter 150 value 459.523438
## iter 160 value 450.505368
## iter 170 value 441.622809
## iter 180 value 432.006703
## iter 190 value 424.805599
## iter 200 value 419.669779
## iter 210 value 414.660947
## iter 220 value 413.318250
## iter 230 value 412.877222
## iter 240 value 412.230969
## iter 250 value 410.796899
## iter 260 value 408.643973
## iter 270 value 406.082772
## iter 280 value 403.208333
## iter 290 value 400.556846
## iter 300 value 397.375323
## iter 310 value 392.567862
## iter 320 value 388.909083
## iter 330 value 386.790814
## iter 340 value 385.907383
## iter 350 value 382.876547
## iter 360 value 380.475740
## iter 370 value 379.114179
## iter 380 value 377.940263
## iter 390 value 377.480319
## iter 400 value 377.106174
## iter 410 value 376.539383
## iter 420 value 376.330141
## iter 430 value 376.276542
## iter 440 value 376.269503
## iter 450 value 376.262611
## iter 460 value 376.243786
## iter 470 value 376.206031
## iter 480 value 376.124653
## iter 490 value 375.977379
## iter 500 value 375.806386
## final value 375.806386
## stopped after 500 iterations
## # weights: 141
## initial value 1356386.438590
## iter 10 value 1506.888527
## iter 20 value 978.485610
## iter 30 value 826.714424
## iter 40 value 738.397106
## iter 50 value 667.866788
## iter 60 value 612.575851
## iter 70 value 573.927537
## iter 80 value 553.403119
## iter 90 value 526.342407
## iter 100 value 488.142545
## iter 110 value 454.814186
## iter 120 value 431.521530
## iter 130 value 420.199413
## iter 140 value 408.194922
## iter 150 value 393.920237
## iter 160 value 380.075578
## iter 170 value 362.301148
## iter 180 value 337.879537
## iter 190 value 319.092985
## iter 200 value 305.086712
## iter 210 value 289.436610
## iter 220 value 275.282778
## iter 230 value 263.088370
## iter 240 value 255.861135
## iter 250 value 250.041495
## iter 260 value 243.849243
## iter 270 value 236.090546
## iter 280 value 230.140358
## iter 290 value 227.886461
## iter 300 value 226.342697
## iter 310 value 224.425217
## iter 320 value 220.846661
## iter 330 value 217.265587
## iter 340 value 210.726473
## iter 350 value 205.568968
## iter 360 value 198.844698
## iter 370 value 191.602181
## iter 380 value 187.115342
## iter 390 value 183.725270
## iter 400 value 179.301109
## iter 410 value 176.980711
## iter 420 value 172.813455
## iter 430 value 169.147296
## iter 440 value 166.695214
## iter 450 value 165.032692
## iter 460 value 162.842445
## iter 470 value 161.621900
## iter 480 value 160.932510
## iter 490 value 160.140090
## iter 500 value 159.626919
## final value 159.626919
## stopped after 500 iterations
## # weights: 15
## initial value 1384763.883320
## iter 10 value 7709.613116
## iter 20 value 5823.399736
## iter 30 value 5774.511454
## iter 40 value 5430.634202
## iter 50 value 3748.385141
## iter 60 value 2172.464199
## iter 70 value 1835.834953
## iter 80 value 1797.941458
## iter 90 value 1761.069461
## iter 100 value 1755.193933
## iter 110 value 1754.084052
## iter 120 value 1753.378876
## iter 130 value 1752.814657
## iter 140 value 1752.357952
## final value 1752.356589
## converged
## # weights: 36
## initial value 1433335.448558
## iter 10 value 42664.911351
## iter 20 value 11963.604967
## iter 30 value 5739.942922
## iter 40 value 4903.331476
## iter 50 value 2780.652397
## iter 60 value 2318.589688
## iter 70 value 1958.132627
## iter 80 value 1795.713278
## iter 90 value 1706.002418
## iter 100 value 1544.410946
## iter 110 value 1534.591106
## iter 120 value 1533.024883
## iter 130 value 1493.273538
## iter 140 value 1473.025962
## iter 150 value 1470.725067
## iter 160 value 1469.965157
## iter 170 value 1465.845417
## iter 180 value 1463.578753
## iter 190 value 1463.555731
## iter 200 value 1463.485148
## iter 210 value 1463.013525
## iter 220 value 1461.948810
## iter 230 value 1461.276859
## iter 240 value 1460.677282
## iter 250 value 1460.545000
## iter 260 value 1460.499188
## iter 270 value 1460.494519
## iter 280 value 1460.468861
## final value 1460.468795
## converged
## # weights: 71
## initial value 1401681.506208
## iter 10 value 2854.215405
## iter 20 value 1487.814304
## iter 30 value 1201.871973
## iter 40 value 1024.282208
## iter 50 value 888.161231
## iter 60 value 803.317577
## iter 70 value 727.537760
## iter 80 value 689.000959
## iter 90 value 673.970894
## iter 100 value 657.883733
## iter 110 value 640.746142
## iter 120 value 628.300597
## iter 130 value 618.870330
## iter 140 value 615.201054
## iter 150 value 613.206542
## iter 160 value 612.776573
## iter 170 value 610.883169
## iter 180 value 607.158440
## iter 190 value 597.743822
## iter 200 value 588.247183
## iter 210 value 581.756431
## iter 220 value 575.689537
## iter 230 value 569.999619
## iter 240 value 567.308965
## iter 250 value 564.315996
## iter 260 value 562.268010
## iter 270 value 561.902758
## iter 280 value 561.832860
## iter 290 value 561.814868
## iter 300 value 561.811727
## iter 310 value 561.808621
## iter 320 value 561.805016
## iter 330 value 561.797760
## iter 340 value 561.782765
## iter 350 value 561.725533
## iter 360 value 561.664280
## iter 370 value 561.563971
## iter 380 value 561.471524
## iter 390 value 561.393688
## iter 400 value 561.349385
## iter 410 value 561.328545
## iter 420 value 561.311099
## iter 430 value 561.273777
## iter 440 value 561.272772
## iter 450 value 561.271119
## iter 460 value 561.267529
## iter 470 value 561.260158
## iter 480 value 561.242793
## iter 490 value 561.214571
## iter 500 value 561.203714
## final value 561.203714
## stopped after 500 iterations
## # weights: 106
## initial value 1449116.096592
## iter 10 value 3524.168720
## iter 20 value 1706.056434
## iter 30 value 1175.241569
## iter 40 value 949.164296
## iter 50 value 800.218551
## iter 60 value 714.812143
## iter 70 value 654.456917
## iter 80 value 604.845858
## iter 90 value 575.604733
## iter 100 value 552.360200
## iter 110 value 520.383974
## iter 120 value 496.063929
## iter 130 value 481.476909
## iter 140 value 475.005205
## iter 150 value 470.704458
## iter 160 value 463.748575
## iter 170 value 458.204308
## iter 180 value 452.466570
## iter 190 value 448.152740
## iter 200 value 444.498272
## iter 210 value 441.570824
## iter 220 value 440.726666
## iter 230 value 439.303303
## iter 240 value 437.816046
## iter 250 value 435.474563
## iter 260 value 432.000325
## iter 270 value 427.115436
## iter 280 value 425.128583
## iter 290 value 423.514197
## iter 300 value 421.094465
## iter 310 value 418.586113
## iter 320 value 416.935557
## iter 330 value 415.238200
## iter 340 value 413.329691
## iter 350 value 411.762591
## iter 360 value 410.146748
## iter 370 value 408.773403
## iter 380 value 408.274991
## iter 390 value 407.680032
## iter 400 value 407.150610
## iter 410 value 406.649517
## iter 420 value 405.060350
## iter 430 value 403.674625
## iter 440 value 403.558472
## iter 450 value 403.372627
## iter 460 value 403.161878
## iter 470 value 402.866513
## iter 480 value 402.580088
## iter 490 value 402.212321
## iter 500 value 401.966677
## final value 401.966677
## stopped after 500 iterations
## # weights: 141
## initial value 1462307.700241
## iter 10 value 1476.895771
## iter 20 value 1063.115693
## iter 30 value 921.095303
## iter 40 value 771.355034
## iter 50 value 671.327010
## iter 60 value 613.877943
## iter 70 value 569.649365
## iter 80 value 499.171489
## iter 90 value 436.780669
## iter 100 value 390.221767
## iter 110 value 363.031769
## iter 120 value 349.958291
## iter 130 value 338.693274
## iter 140 value 323.473449
## iter 150 value 306.922440
## iter 160 value 296.666199
## iter 170 value 288.678084
## iter 180 value 277.862885
## iter 190 value 267.448201
## iter 200 value 260.127440
## iter 210 value 253.774820
## iter 220 value 249.223254
## iter 230 value 245.940086
## iter 240 value 239.333908
## iter 250 value 233.388802
## iter 260 value 227.590944
## iter 270 value 222.207832
## iter 280 value 215.747398
## iter 290 value 213.112564
## iter 300 value 211.772907
## iter 310 value 209.216559
## iter 320 value 206.560042
## iter 330 value 201.043360
## iter 340 value 195.686972
## iter 350 value 193.049015
## iter 360 value 191.195361
## iter 370 value 189.194753
## iter 380 value 185.021034
## iter 390 value 179.175786
## iter 400 value 174.751843
## iter 410 value 168.371510
## iter 420 value 163.089337
## iter 430 value 160.641851
## iter 440 value 157.782823
## iter 450 value 155.574119
## iter 460 value 153.662207
## iter 470 value 152.531811
## iter 480 value 152.035849
## iter 490 value 151.848877
## iter 500 value 151.620446
## final value 151.620446
## stopped after 500 iterations
## # weights: 15
## initial value 1381310.525640
## iter 10 value 184370.825597
## iter 20 value 15721.272210
## iter 30 value 11172.698691
## iter 40 value 11001.786749
## final value 11001.231031
## converged
## # weights: 36
## initial value 1410413.643841
## iter 10 value 32670.958696
## iter 20 value 5985.176422
## iter 30 value 4062.968108
## iter 40 value 3603.317766
## iter 50 value 2455.779456
## iter 60 value 2038.166763
## iter 70 value 2015.887271
## iter 80 value 2013.609241
## iter 90 value 2010.928127
## iter 100 value 1916.382925
## iter 110 value 1911.572087
## iter 120 value 1907.030525
## iter 130 value 1720.279669
## iter 140 value 1540.677944
## iter 150 value 1462.763899
## iter 160 value 1437.279325
## iter 170 value 1420.936912
## iter 180 value 1408.317281
## iter 190 value 1401.429380
## iter 200 value 1400.333752
## iter 210 value 1400.027423
## iter 220 value 1399.760565
## iter 230 value 1398.276603
## iter 240 value 1396.323755
## iter 250 value 1394.802856
## iter 260 value 1385.170117
## iter 270 value 1335.168268
## iter 280 value 1310.131828
## iter 290 value 1291.711755
## iter 300 value 1218.464369
## iter 310 value 1160.446544
## iter 320 value 1131.307474
## iter 330 value 1121.482658
## iter 340 value 1118.833424
## iter 350 value 1117.327987
## iter 360 value 1116.995894
## iter 370 value 1115.660457
## iter 380 value 1113.325088
## iter 390 value 1111.638535
## iter 400 value 1111.069706
## iter 410 value 1110.179662
## iter 420 value 1109.640260
## iter 430 value 1109.603825
## iter 440 value 1108.612187
## iter 450 value 1106.903230
## iter 460 value 1099.437427
## iter 470 value 1099.145668
## iter 480 value 1099.069776
## iter 490 value 1099.052372
## iter 500 value 1098.988013
## final value 1098.988013
## stopped after 500 iterations
## # weights: 71
## initial value 1394447.666814
## iter 10 value 2826.643814
## iter 20 value 1833.045391
## iter 30 value 1441.059567
## iter 40 value 1186.198686
## iter 50 value 1122.980105
## iter 60 value 990.774449
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## iter 470 value 663.719206
## iter 480 value 663.635248
## iter 490 value 663.575065
## iter 500 value 663.560773
## final value 663.560773
## stopped after 500 iterations
## # weights: 106
## initial value 1377845.062161
## iter 10 value 1451.872650
## iter 20 value 1048.465775
## iter 30 value 941.434261
## iter 40 value 833.039762
## iter 50 value 790.930781
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## iter 70 value 671.843922
## iter 80 value 613.133013
## iter 90 value 576.317084
## iter 100 value 537.065821
## iter 110 value 509.144136
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## iter 180 value 404.786707
## iter 190 value 395.269877
## iter 200 value 387.668751
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## iter 250 value 374.045130
## iter 260 value 370.042030
## iter 270 value 366.786100
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## iter 290 value 355.554972
## iter 300 value 351.229886
## iter 310 value 343.293849
## iter 320 value 337.288438
## iter 330 value 330.580207
## iter 340 value 319.617001
## iter 350 value 309.011403
## iter 360 value 302.039542
## iter 370 value 293.588705
## iter 380 value 287.556545
## iter 390 value 283.258416
## iter 400 value 282.079198
## iter 410 value 281.582408
## iter 420 value 279.368843
## iter 430 value 277.348277
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## iter 450 value 276.994981
## iter 460 value 276.367440
## iter 470 value 275.532852
## iter 480 value 274.567877
## iter 490 value 273.641980
## iter 500 value 273.332181
## final value 273.332181
## stopped after 500 iterations
## # weights: 141
## initial value 1409418.855055
## iter 10 value 1497.388267
## iter 20 value 1072.109825
## iter 30 value 885.309188
## iter 40 value 798.911587
## iter 50 value 741.490090
## iter 60 value 689.951782
## iter 70 value 607.433960
## iter 80 value 561.055202
## iter 90 value 505.002556
## iter 100 value 476.023076
## iter 110 value 439.591960
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## iter 130 value 404.256262
## iter 140 value 391.358798
## iter 150 value 373.322501
## iter 160 value 362.422480
## iter 170 value 356.510595
## iter 180 value 350.837281
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## iter 200 value 340.311211
## iter 210 value 323.330128
## iter 220 value 309.196291
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## iter 300 value 248.399695
## iter 310 value 245.078835
## iter 320 value 241.816856
## iter 330 value 239.177883
## iter 340 value 237.054524
## iter 350 value 234.528651
## iter 360 value 230.029685
## iter 370 value 226.219806
## iter 380 value 218.989053
## iter 390 value 208.629102
## iter 400 value 201.731775
## iter 410 value 196.280224
## iter 420 value 191.298374
## iter 430 value 184.559472
## iter 440 value 177.169356
## iter 450 value 172.989802
## iter 460 value 171.461455
## iter 470 value 168.946556
## iter 480 value 167.724256
## iter 490 value 166.510564
## iter 500 value 165.788840
## final value 165.788840
## stopped after 500 iterations
## # weights: 15
## initial value 1389940.740895
## iter 10 value 9957.104566
## iter 20 value 6811.969095
## iter 30 value 4157.058720
## iter 40 value 3859.551176
## iter 50 value 3506.945601
## iter 60 value 2518.548077
## iter 70 value 2230.526366
## iter 80 value 2166.642866
## iter 90 value 2125.440613
## iter 100 value 1890.545496
## iter 110 value 1750.353042
## iter 120 value 1706.437328
## iter 130 value 1678.778925
## iter 140 value 1540.241014
## iter 150 value 1503.841040
## iter 160 value 1493.883499
## iter 170 value 1479.923571
## iter 180 value 1479.266851
## iter 190 value 1479.260791
## final value 1479.259761
## converged
## # weights: 36
## initial value 1377192.040010
## iter 10 value 8206.928004
## iter 20 value 4747.656249
## iter 30 value 3849.258071
## iter 40 value 3335.763157
## iter 50 value 2781.027802
## iter 60 value 2148.715607
## iter 70 value 1643.980617
## iter 80 value 1601.475291
## iter 90 value 1448.126870
## iter 100 value 1315.531294
## iter 110 value 1268.228531
## iter 120 value 1247.851899
## iter 130 value 1220.169504
## iter 140 value 1211.048350
## iter 150 value 1202.802349
## iter 160 value 1200.585349
## iter 170 value 1194.113065
## iter 180 value 1191.520538
## iter 190 value 1191.446954
## iter 190 value 1191.446948
## iter 190 value 1191.446948
## final value 1191.446948
## converged
## # weights: 71
## initial value 1421186.199171
## iter 10 value 2270.143658
## iter 20 value 1287.836057
## iter 30 value 1117.254077
## iter 40 value 1061.520496
## iter 50 value 1011.991890
## iter 60 value 985.568951
## iter 70 value 965.506736
## iter 80 value 936.931759
## iter 90 value 921.505914
## iter 100 value 915.358551
## iter 110 value 908.873493
## iter 120 value 899.396854
## iter 130 value 890.312828
## iter 140 value 885.065368
## iter 150 value 880.378233
## iter 160 value 877.696333
## iter 170 value 872.260532
## iter 180 value 868.675828
## iter 190 value 866.533102
## iter 200 value 865.245994
## iter 210 value 862.099783
## iter 220 value 852.971274
## iter 230 value 842.956800
## iter 240 value 841.777857
## iter 250 value 841.682616
## iter 260 value 841.039158
## iter 270 value 838.514937
## iter 280 value 837.744798
## iter 290 value 837.572287
## iter 300 value 837.569315
## iter 310 value 837.567939
## iter 320 value 837.566484
## iter 330 value 837.565088
## iter 340 value 837.564339
## iter 350 value 837.563990
## final value 837.563968
## converged
## # weights: 106
## initial value 1458357.223224
## iter 10 value 2650.438708
## iter 20 value 1390.316058
## iter 30 value 1179.426958
## iter 40 value 1078.408409
## iter 50 value 969.755398
## iter 60 value 908.456597
## iter 70 value 856.850537
## iter 80 value 831.129298
## iter 90 value 806.745293
## iter 100 value 788.407594
## iter 110 value 765.438546
## iter 120 value 731.292665
## iter 130 value 714.241555
## iter 140 value 704.463430
## iter 150 value 699.519538
## iter 160 value 697.142361
## iter 170 value 695.026594
## iter 180 value 693.583258
## iter 190 value 692.698833
## iter 200 value 691.377784
## iter 210 value 690.068715
## iter 220 value 689.563755
## iter 230 value 688.624554
## iter 240 value 687.347886
## iter 250 value 686.883236
## iter 260 value 686.629247
## iter 270 value 686.160763
## iter 280 value 682.914465
## iter 290 value 677.866397
## iter 300 value 673.668326
## iter 310 value 669.345711
## iter 320 value 666.599214
## iter 330 value 665.535321
## iter 340 value 665.032655
## iter 350 value 664.895299
## iter 360 value 664.889353
## iter 370 value 664.888947
## final value 664.888734
## converged
## # weights: 141
## initial value 1482179.119857
## iter 10 value 1589.200992
## iter 20 value 1209.800561
## iter 30 value 1025.775127
## iter 40 value 932.003206
## iter 50 value 851.903898
## iter 60 value 778.138444
## iter 70 value 743.885754
## iter 80 value 706.370840
## iter 90 value 683.082198
## iter 100 value 662.672223
## iter 110 value 648.697112
## iter 120 value 640.045876
## iter 130 value 631.821129
## iter 140 value 623.775969
## iter 150 value 616.631496
## iter 160 value 612.598898
## iter 170 value 609.317352
## iter 180 value 604.708204
## iter 190 value 599.811574
## iter 200 value 593.373256
## iter 210 value 582.025036
## iter 220 value 575.090261
## iter 230 value 570.716678
## iter 240 value 565.288739
## iter 250 value 556.320603
## iter 260 value 550.199671
## iter 270 value 545.737524
## iter 280 value 542.086459
## iter 290 value 540.826504
## iter 300 value 539.078972
## iter 310 value 536.705876
## iter 320 value 535.415073
## iter 330 value 534.843660
## iter 340 value 534.329765
## iter 350 value 533.836938
## iter 360 value 532.414704
## iter 370 value 528.686253
## iter 380 value 526.045872
## iter 390 value 525.100521
## iter 400 value 523.919094
## iter 410 value 522.178684
## iter 420 value 521.334508
## iter 430 value 520.923906
## iter 440 value 520.686507
## iter 450 value 520.445265
## iter 460 value 520.287517
## iter 470 value 520.135982
## iter 480 value 519.623294
## iter 490 value 518.316619
## iter 500 value 517.477285
## final value 517.477285
## stopped after 500 iterations
## # weights: 15
## initial value 1460601.304015
## iter 10 value 10845.612278
## iter 20 value 5993.198342
## iter 30 value 3433.962091
## iter 40 value 2928.356814
## iter 50 value 1820.880171
## iter 60 value 1357.694256
## iter 70 value 1300.219164
## iter 80 value 1240.196139
## iter 90 value 1222.548569
## iter 100 value 1216.767215
## iter 110 value 1212.267401
## iter 120 value 1205.911100
## iter 130 value 1203.921042
## iter 140 value 1203.405736
## iter 150 value 1202.597981
## final value 1202.588125
## converged
## # weights: 36
## initial value 1386049.109483
## iter 10 value 5560.070422
## iter 20 value 3308.235498
## iter 30 value 2723.605917
## iter 40 value 2390.973078
## iter 50 value 2053.048943
## iter 60 value 1838.203934
## iter 70 value 1560.640776
## iter 80 value 1382.781268
## iter 90 value 1241.549306
## iter 100 value 1209.984476
## iter 110 value 1189.113345
## iter 120 value 1171.062287
## iter 130 value 1162.547225
## iter 140 value 1135.473078
## iter 150 value 1061.637193
## iter 160 value 1037.205400
## iter 170 value 980.467976
## iter 180 value 945.452252
## iter 190 value 936.328078
## iter 200 value 936.240271
## iter 210 value 936.092762
## iter 220 value 935.510123
## iter 230 value 934.805458
## iter 240 value 934.766385
## iter 250 value 934.762276
## iter 250 value 934.762267
## iter 250 value 934.762267
## final value 934.762267
## converged
## # weights: 71
## initial value 1423258.262944
## iter 10 value 2920.279031
## iter 20 value 1496.410974
## iter 30 value 1068.457708
## iter 40 value 965.241150
## iter 50 value 916.486679
## iter 60 value 871.761186
## iter 70 value 839.288220
## iter 80 value 818.364130
## iter 90 value 806.435723
## iter 100 value 792.996491
## iter 110 value 779.629277
## iter 120 value 772.532256
## iter 130 value 763.287808
## iter 140 value 754.114248
## iter 150 value 750.249048
## iter 160 value 741.709307
## iter 170 value 727.827930
## iter 180 value 714.500792
## iter 190 value 706.468660
## iter 200 value 696.559419
## iter 210 value 683.287811
## iter 220 value 671.861897
## iter 230 value 666.893207
## iter 240 value 664.085524
## iter 250 value 663.631153
## iter 260 value 663.366670
## iter 270 value 663.123084
## iter 280 value 662.988047
## iter 290 value 662.844573
## iter 300 value 662.825733
## iter 310 value 662.781841
## iter 320 value 662.741437
## iter 330 value 662.552569
## iter 340 value 662.234814
## iter 350 value 661.915456
## iter 360 value 661.731221
## iter 370 value 661.539484
## iter 380 value 661.484411
## iter 390 value 661.443712
## iter 400 value 661.419390
## iter 410 value 660.847146
## iter 420 value 660.422768
## iter 430 value 660.224488
## iter 440 value 660.207339
## iter 450 value 660.152524
## iter 460 value 659.955246
## iter 470 value 659.095394
## iter 480 value 658.711023
## iter 490 value 658.606856
## iter 500 value 658.477546
## final value 658.477546
## stopped after 500 iterations
## # weights: 106
## initial value 1347528.453424
## iter 10 value 1382.944585
## iter 20 value 1145.173218
## iter 30 value 955.624448
## iter 40 value 887.371954
## iter 50 value 822.553812
## iter 60 value 733.526786
## iter 70 value 685.481743
## iter 80 value 627.381122
## iter 90 value 588.142681
## iter 100 value 550.496479
## iter 110 value 524.180413
## iter 120 value 516.558335
## iter 130 value 506.573196
## iter 140 value 496.584657
## iter 150 value 480.199127
## iter 160 value 469.275325
## iter 170 value 459.163096
## iter 180 value 450.583451
## iter 190 value 436.234000
## iter 200 value 429.628273
## iter 210 value 426.342400
## iter 220 value 423.912178
## iter 230 value 420.848633
## iter 240 value 415.754128
## iter 250 value 404.584755
## iter 260 value 395.758275
## iter 270 value 390.693751
## iter 280 value 384.542451
## iter 290 value 379.733840
## iter 300 value 375.824221
## iter 310 value 373.130574
## iter 320 value 371.511155
## iter 330 value 370.682927
## iter 340 value 370.272343
## iter 350 value 370.227577
## iter 360 value 370.217587
## iter 370 value 370.213776
## iter 380 value 370.212131
## iter 390 value 370.211417
## iter 400 value 370.211117
## final value 370.211080
## converged
## # weights: 141
## initial value 1447720.435724
## iter 10 value 1380.455883
## iter 20 value 1096.680256
## iter 30 value 943.796371
## iter 40 value 819.854950
## iter 50 value 730.111775
## iter 60 value 624.753192
## iter 70 value 561.658234
## iter 80 value 500.622888
## iter 90 value 449.636978
## iter 100 value 408.997137
## iter 110 value 375.047197
## iter 120 value 355.358987
## iter 130 value 340.769838
## iter 140 value 324.842520
## iter 150 value 304.097478
## iter 160 value 293.421824
## iter 170 value 284.906374
## iter 180 value 280.754635
## iter 190 value 276.633769
## iter 200 value 272.420532
## iter 210 value 268.583599
## iter 220 value 265.071700
## iter 230 value 262.727775
## iter 240 value 260.709253
## iter 250 value 256.814446
## iter 260 value 251.884876
## iter 270 value 248.764738
## iter 280 value 246.287595
## iter 290 value 245.295705
## iter 300 value 244.629137
## iter 310 value 243.319949
## iter 320 value 242.327345
## iter 330 value 240.725145
## iter 340 value 237.717675
## iter 350 value 235.261471
## iter 360 value 231.683225
## iter 370 value 228.473136
## iter 380 value 226.201439
## iter 390 value 224.920342
## iter 400 value 223.886913
## iter 410 value 223.067058
## iter 420 value 222.064956
## iter 430 value 221.605381
## iter 440 value 220.120005
## iter 450 value 214.172930
## iter 460 value 212.879307
## iter 470 value 212.233874
## iter 480 value 211.984394
## iter 490 value 211.842031
## iter 500 value 211.770536
## final value 211.770536
## stopped after 500 iterations
## # weights: 15
## initial value 1395117.114379
## iter 10 value 5573.911464
## iter 20 value 5373.943474
## iter 30 value 5082.060489
## iter 40 value 4693.537585
## iter 50 value 4246.704092
## iter 60 value 3482.209754
## iter 70 value 1806.018618
## iter 80 value 1596.093289
## iter 90 value 1280.091187
## iter 100 value 1231.069426
## iter 110 value 1230.468305
## iter 120 value 1216.221651
## iter 130 value 1205.715527
## iter 140 value 1205.344869
## iter 150 value 1202.787255
## iter 160 value 1199.790329
## iter 170 value 1199.549015
## iter 180 value 1198.932665
## iter 190 value 1198.013387
## iter 200 value 1197.931163
## iter 210 value 1197.892756
## iter 220 value 1197.756518
## final value 1197.754722
## converged
## # weights: 36
## initial value 1379593.852359
## iter 10 value 6612.326334
## iter 20 value 3379.828877
## iter 30 value 2627.730222
## iter 40 value 2054.781541
## iter 50 value 1751.934847
## iter 60 value 1614.494965
## iter 70 value 1550.486507
## iter 80 value 1499.603219
## iter 90 value 1450.359980
## iter 100 value 1400.779627
## iter 110 value 1237.642239
## iter 120 value 1166.209526
## iter 130 value 1145.249601
## iter 140 value 1143.104619
## iter 150 value 1142.209020
## iter 160 value 1141.708577
## iter 170 value 1141.573814
## iter 180 value 1141.452003
## iter 190 value 1141.244262
## iter 200 value 1140.966160
## iter 210 value 1140.888086
## iter 220 value 1140.797805
## iter 230 value 1140.739534
## final value 1140.725278
## converged
## # weights: 71
## initial value 1408035.109725
## iter 10 value 5552.077174
## iter 20 value 2361.826117
## iter 30 value 1581.244059
## iter 40 value 1310.866001
## iter 50 value 1068.153027
## iter 60 value 993.523878
## iter 70 value 924.454721
## iter 80 value 903.824762
## iter 90 value 858.890838
## iter 100 value 808.871928
## iter 110 value 787.261738
## iter 120 value 772.834143
## iter 130 value 769.749350
## iter 140 value 767.665152
## iter 150 value 767.174416
## iter 160 value 767.105481
## iter 170 value 766.938027
## iter 180 value 766.691497
## iter 190 value 765.138805
## iter 200 value 763.344142
## iter 210 value 760.543632
## iter 220 value 744.887509
## iter 230 value 718.439613
## iter 240 value 672.402077
## iter 250 value 656.493841
## iter 260 value 653.648861
## iter 270 value 653.042136
## iter 280 value 651.476944
## iter 290 value 649.949663
## iter 300 value 645.932168
## iter 310 value 640.642831
## iter 320 value 630.178241
## iter 330 value 617.020363
## iter 340 value 604.642938
## iter 350 value 599.532930
## iter 360 value 597.520000
## iter 370 value 596.578439
## iter 380 value 594.545620
## iter 390 value 593.481215
## iter 400 value 592.845240
## iter 410 value 592.726385
## iter 420 value 592.595018
## iter 430 value 592.119317
## iter 440 value 587.604893
## iter 450 value 585.347784
## iter 460 value 573.218551
## iter 470 value 566.069619
## iter 480 value 559.791449
## iter 490 value 556.161288
## iter 500 value 555.700304
## final value 555.700304
## stopped after 500 iterations
## # weights: 106
## initial value 1481931.662579
## iter 10 value 1788.057403
## iter 20 value 1144.757178
## iter 30 value 947.477339
## iter 40 value 826.454253
## iter 50 value 753.277889
## iter 60 value 689.485542
## iter 70 value 632.086125
## iter 80 value 589.056226
## iter 90 value 555.001617
## iter 100 value 527.257388
## iter 110 value 508.216162
## iter 120 value 485.008446
## iter 130 value 460.735692
## iter 140 value 449.900788
## iter 150 value 438.661894
## iter 160 value 427.993808
## iter 170 value 420.415600
## iter 180 value 409.580102
## iter 190 value 395.557814
## iter 200 value 389.355945
## iter 210 value 384.194798
## iter 220 value 380.985031
## iter 230 value 376.819261
## iter 240 value 371.607751
## iter 250 value 368.001113
## iter 260 value 362.403183
## iter 270 value 355.963157
## iter 280 value 347.533128
## iter 290 value 339.272426
## iter 300 value 329.717481
## iter 310 value 325.812621
## iter 320 value 323.397915
## iter 330 value 320.106031
## iter 340 value 317.850935
## iter 350 value 317.012738
## iter 360 value 316.583239
## iter 370 value 316.011412
## iter 380 value 315.525706
## iter 390 value 313.647982
## iter 400 value 311.487533
## iter 410 value 310.246526
## iter 420 value 309.118465
## iter 430 value 308.544993
## iter 440 value 308.489135
## iter 450 value 308.432721
## iter 460 value 308.382531
## iter 470 value 308.347906
## iter 480 value 308.281954
## iter 490 value 308.123232
## iter 500 value 307.536282
## final value 307.536282
## stopped after 500 iterations
## # weights: 141
## initial value 1412261.758369
## iter 10 value 1688.400932
## iter 20 value 1153.671944
## iter 30 value 975.153052
## iter 40 value 840.663132
## iter 50 value 677.691954
## iter 60 value 604.014355
## iter 70 value 533.126764
## iter 80 value 452.181835
## iter 90 value 431.568611
## iter 100 value 410.775856
## iter 110 value 391.430098
## iter 120 value 375.066860
## iter 130 value 361.954439
## iter 140 value 351.615831
## iter 150 value 338.250356
## iter 160 value 325.224783
## iter 170 value 309.828655
## iter 180 value 291.820143
## iter 190 value 269.817536
## iter 200 value 257.849241
## iter 210 value 246.889675
## iter 220 value 236.868028
## iter 230 value 231.522319
## iter 240 value 227.365233
## iter 250 value 222.008124
## iter 260 value 216.929525
## iter 270 value 212.696346
## iter 280 value 209.348707
## iter 290 value 207.922812
## iter 300 value 207.024516
## iter 310 value 205.501769
## iter 320 value 204.234290
## iter 330 value 202.447109
## iter 340 value 200.343652
## iter 350 value 197.830967
## iter 360 value 194.332959
## iter 370 value 191.326479
## iter 380 value 187.322453
## iter 390 value 183.707182
## iter 400 value 179.843003
## iter 410 value 177.723711
## iter 420 value 174.476939
## iter 430 value 172.104428
## iter 440 value 170.379525
## iter 450 value 168.322601
## iter 460 value 167.024371
## iter 470 value 165.682621
## iter 480 value 165.400846
## iter 490 value 164.938005
## iter 500 value 163.858923
## final value 163.858923
## stopped after 500 iterations
## # weights: 15
## initial value 1408337.019516
## iter 10 value 6824.951014
## iter 20 value 2344.949372
## iter 30 value 1616.638683
## iter 40 value 1599.129596
## iter 50 value 1539.351606
## iter 60 value 1509.819385
## iter 70 value 1504.455454
## iter 80 value 1498.605015
## iter 90 value 1484.554769
## iter 100 value 1480.187549
## iter 110 value 1477.959313
## iter 120 value 1472.202352
## iter 130 value 1471.664487
## iter 140 value 1471.418285
## iter 150 value 1469.860182
## iter 160 value 1469.129222
## iter 170 value 1468.587170
## iter 180 value 1466.945421
## iter 190 value 1466.622794
## iter 200 value 1466.582863
## iter 210 value 1466.031611
## iter 220 value 1465.579691
## iter 230 value 1465.459074
## iter 240 value 1464.488454
## iter 250 value 1464.221267
## iter 260 value 1464.217676
## final value 1464.217371
## converged
## # weights: 36
## initial value 1425167.244067
## iter 10 value 22944.644977
## iter 20 value 15437.894358
## iter 30 value 8921.251755
## iter 40 value 5121.262438
## iter 50 value 2495.995549
## iter 60 value 2301.895436
## iter 70 value 2110.763849
## iter 80 value 1883.726124
## iter 90 value 1723.570853
## iter 100 value 1618.978671
## iter 110 value 1482.745147
## iter 120 value 1451.631692
## iter 130 value 1441.221251
## iter 140 value 1428.944816
## iter 150 value 1426.826997
## iter 160 value 1424.245625
## iter 170 value 1424.077828
## iter 180 value 1422.391008
## iter 190 value 1418.410627
## iter 200 value 1416.332461
## iter 210 value 1413.508181
## iter 220 value 1411.720369
## iter 230 value 1410.047818
## final value 1410.024497
## converged
## # weights: 71
## initial value 1402787.659337
## iter 10 value 2139.335295
## iter 20 value 1233.339988
## iter 30 value 1102.449878
## iter 40 value 1003.989120
## iter 50 value 952.536592
## iter 60 value 907.688488
## iter 70 value 843.327823
## iter 80 value 818.014758
## iter 90 value 790.047569
## iter 100 value 770.554791
## iter 110 value 763.038802
## iter 120 value 757.118456
## iter 130 value 752.237938
## iter 140 value 746.107912
## iter 150 value 744.166768
## iter 160 value 742.454876
## iter 170 value 740.125753
## iter 180 value 736.870311
## iter 190 value 733.288067
## iter 200 value 721.830832
## iter 210 value 699.014664
## iter 220 value 683.134763
## iter 230 value 671.245587
## iter 240 value 658.528954
## iter 250 value 645.079206
## iter 260 value 635.997893
## iter 270 value 631.462283
## iter 280 value 627.255604
## iter 290 value 625.367804
## iter 300 value 624.541489
## iter 310 value 623.155179
## iter 320 value 619.368524
## iter 330 value 615.042497
## iter 340 value 609.505450
## iter 350 value 604.553953
## iter 360 value 601.665413
## iter 370 value 598.974049
## iter 380 value 597.040326
## iter 390 value 596.251802
## iter 400 value 595.469277
## iter 410 value 594.156251
## iter 420 value 589.519005
## iter 430 value 582.757583
## iter 440 value 580.127772
## iter 450 value 578.836864
## iter 460 value 576.068673
## iter 470 value 569.564480
## iter 480 value 565.064446
## iter 490 value 562.773166
## iter 500 value 561.997817
## final value 561.997817
## stopped after 500 iterations
## # weights: 106
## initial value 1423378.459551
## iter 10 value 1377.074981
## iter 20 value 1097.183045
## iter 30 value 959.382278
## iter 40 value 845.301732
## iter 50 value 754.050693
## iter 60 value 688.431840
## iter 70 value 629.499535
## iter 80 value 559.324227
## iter 90 value 520.294048
## iter 100 value 498.700802
## iter 110 value 471.483228
## iter 120 value 452.152360
## iter 130 value 429.687664
## iter 140 value 410.093096
## iter 150 value 399.368782
## iter 160 value 392.324369
## iter 170 value 388.206879
## iter 180 value 383.876188
## iter 190 value 379.172586
## iter 200 value 376.459270
## iter 210 value 373.542895
## iter 220 value 371.709141
## iter 230 value 370.683866
## iter 240 value 368.439784
## iter 250 value 365.603425
## iter 260 value 360.484459
## iter 270 value 353.188138
## iter 280 value 346.364478
## iter 290 value 334.873859
## iter 300 value 328.001820
## iter 310 value 323.700707
## iter 320 value 317.134865
## iter 330 value 311.445542
## iter 340 value 308.125513
## iter 350 value 306.012470
## iter 360 value 303.351582
## iter 370 value 299.955389
## iter 380 value 294.867113
## iter 390 value 292.039991
## iter 400 value 288.777171
## iter 410 value 285.387811
## iter 420 value 284.543460
## iter 430 value 283.872564
## iter 440 value 283.796577
## iter 450 value 283.740394
## iter 460 value 283.683711
## iter 470 value 283.632376
## iter 480 value 283.563969
## iter 490 value 283.456389
## iter 500 value 283.290178
## final value 283.290178
## stopped after 500 iterations
## # weights: 141
## initial value 1454681.732369
## iter 10 value 3136.383843
## iter 20 value 1468.363303
## iter 30 value 1147.435707
## iter 40 value 1020.054075
## iter 50 value 918.211394
## iter 60 value 879.660817
## iter 70 value 841.471708
## iter 80 value 773.037328
## iter 90 value 706.920798
## iter 100 value 675.449155
## iter 110 value 658.913148
## iter 120 value 623.836854
## iter 130 value 580.568717
## iter 140 value 555.299135
## iter 150 value 536.754261
## iter 160 value 506.529893
## iter 170 value 475.020203
## iter 180 value 439.714266
## iter 190 value 419.342574
## iter 200 value 408.697825
## iter 210 value 395.369381
## iter 220 value 372.170533
## iter 230 value 363.248617
## iter 240 value 353.452445
## iter 250 value 346.552086
## iter 260 value 336.152194
## iter 270 value 331.085999
## iter 280 value 327.249263
## iter 290 value 325.567630
## iter 300 value 325.130344
## iter 310 value 324.259470
## iter 320 value 322.754446
## iter 330 value 320.357736
## iter 340 value 317.334262
## iter 350 value 312.517305
## iter 360 value 303.374180
## iter 370 value 293.179202
## iter 380 value 287.624872
## iter 390 value 280.916699
## iter 400 value 271.533159
## iter 410 value 267.025220
## iter 420 value 263.675665
## iter 430 value 261.402399
## iter 440 value 259.359348
## iter 450 value 258.196668
## iter 460 value 257.619550
## iter 470 value 256.540648
## iter 480 value 254.214670
## iter 490 value 252.590476
## iter 500 value 251.335811
## final value 251.335811
## stopped after 500 iterations
## # weights: 15
## initial value 1402695.316282
## iter 10 value 6380.352761
## iter 20 value 6002.326924
## iter 30 value 5980.748924
## iter 40 value 5391.519870
## iter 50 value 2797.764418
## iter 60 value 1899.642602
## iter 70 value 1805.568552
## iter 80 value 1743.800828
## iter 90 value 1733.607811
## iter 100 value 1730.180347
## iter 110 value 1725.973992
## iter 120 value 1723.985045
## iter 130 value 1723.667320
## iter 140 value 1721.284011
## iter 150 value 1718.532893
## iter 160 value 1716.080746
## iter 170 value 1715.355982
## final value 1715.354998
## converged
## # weights: 36
## initial value 1402115.490621
## iter 10 value 37457.283603
## iter 20 value 4746.373495
## iter 30 value 4508.758656
## iter 40 value 4477.381341
## iter 50 value 4469.324491
## iter 60 value 4469.159192
## iter 70 value 4467.720435
## iter 80 value 4467.398012
## iter 90 value 4466.614499
## iter 100 value 4463.567578
## iter 110 value 4459.324481
## iter 120 value 4441.872973
## iter 130 value 4391.902141
## iter 140 value 4269.244472
## iter 150 value 3806.950846
## iter 160 value 2855.690192
## iter 170 value 1995.006446
## iter 180 value 1830.326712
## iter 190 value 1766.109259
## iter 200 value 1735.490826
## iter 210 value 1720.479163
## iter 220 value 1691.708398
## iter 230 value 1688.442968
## iter 240 value 1684.053231
## iter 250 value 1660.984423
## iter 260 value 1637.962624
## iter 270 value 1633.619952
## iter 280 value 1621.569095
## iter 290 value 1601.876626
## iter 300 value 1563.497877
## iter 310 value 1544.059958
## iter 320 value 1540.367313
## iter 330 value 1537.667897
## iter 340 value 1536.758936
## iter 350 value 1531.208435
## iter 360 value 1527.663359
## iter 370 value 1507.376364
## iter 380 value 1506.485420
## iter 390 value 1505.832306
## iter 400 value 1505.045965
## iter 410 value 1504.275794
## iter 420 value 1503.730055
## iter 430 value 1503.236974
## iter 440 value 1502.737656
## iter 450 value 1502.712578
## iter 450 value 1502.712574
## final value 1502.712092
## converged
## # weights: 71
## initial value 1393289.411761
## iter 10 value 4715.848397
## iter 20 value 2106.483288
## iter 30 value 1625.924165
## iter 40 value 1352.253325
## iter 50 value 1080.157973
## iter 60 value 947.041363
## iter 70 value 791.249133
## iter 80 value 712.079736
## iter 90 value 678.361034
## iter 100 value 664.725343
## iter 110 value 656.667570
## iter 120 value 649.704592
## iter 130 value 645.387812
## iter 140 value 636.312946
## iter 150 value 632.996933
## iter 160 value 631.598238
## iter 170 value 627.059316
## iter 180 value 620.045257
## iter 190 value 613.328928
## iter 200 value 593.534422
## iter 210 value 581.817028
## iter 220 value 571.205139
## iter 230 value 564.966814
## iter 240 value 562.556104
## iter 250 value 560.892197
## iter 260 value 558.994814
## iter 270 value 556.731284
## iter 280 value 555.208620
## iter 290 value 554.899883
## iter 300 value 554.884008
## iter 310 value 554.866694
## iter 320 value 554.797957
## iter 330 value 554.728978
## iter 340 value 554.710732
## iter 350 value 554.638751
## iter 360 value 554.445114
## iter 370 value 554.368936
## iter 380 value 554.273082
## iter 390 value 554.203010
## iter 400 value 554.140211
## iter 410 value 554.117987
## iter 420 value 554.065932
## iter 430 value 554.040054
## iter 440 value 554.038469
## iter 450 value 554.036801
## iter 460 value 554.034939
## iter 470 value 554.020212
## iter 480 value 554.004281
## iter 490 value 554.000690
## iter 500 value 553.991954
## final value 553.991954
## stopped after 500 iterations
## # weights: 106
## initial value 1403306.937650
## iter 10 value 1489.807939
## iter 20 value 1118.596824
## iter 30 value 916.295527
## iter 40 value 826.286299
## iter 50 value 756.741621
## iter 60 value 719.360531
## iter 70 value 673.942788
## iter 80 value 639.978483
## iter 90 value 600.701460
## iter 100 value 564.639069
## iter 110 value 540.355896
## iter 120 value 523.115761
## iter 130 value 501.586409
## iter 140 value 490.563376
## iter 150 value 480.253887
## iter 160 value 455.425952
## iter 170 value 435.455089
## iter 180 value 414.290597
## iter 190 value 390.477930
## iter 200 value 373.551827
## iter 210 value 361.995593
## iter 220 value 356.848363
## iter 230 value 354.638246
## iter 240 value 351.518882
## iter 250 value 346.375828
## iter 260 value 341.692049
## iter 270 value 336.120931
## iter 280 value 332.173052
## iter 290 value 327.864140
## iter 300 value 322.130651
## iter 310 value 317.172009
## iter 320 value 313.556966
## iter 330 value 302.769680
## iter 340 value 295.663151
## iter 350 value 290.818510
## iter 360 value 288.719066
## iter 370 value 286.828947
## iter 380 value 285.030791
## iter 390 value 283.897375
## iter 400 value 282.666274
## iter 410 value 282.038230
## iter 420 value 281.638377
## iter 430 value 281.094259
## iter 440 value 281.042650
## iter 450 value 280.966822
## iter 460 value 280.734646
## iter 470 value 280.122408
## iter 480 value 279.632591
## iter 490 value 279.255727
## iter 500 value 279.135127
## final value 279.135127
## stopped after 500 iterations
## # weights: 141
## initial value 1393306.512503
## iter 10 value 1617.631497
## iter 20 value 1051.249889
## iter 30 value 882.402373
## iter 40 value 761.195814
## iter 50 value 658.833216
## iter 60 value 587.677486
## iter 70 value 546.008241
## iter 80 value 497.987167
## iter 90 value 459.704980
## iter 100 value 429.703528
## iter 110 value 390.436897
## iter 120 value 362.546069
## iter 130 value 342.328978
## iter 140 value 321.231684
## iter 150 value 295.873518
## iter 160 value 272.439508
## iter 170 value 247.664324
## iter 180 value 237.300497
## iter 190 value 228.925650
## iter 200 value 223.642609
## iter 210 value 218.593996
## iter 220 value 213.977332
## iter 230 value 210.851311
## iter 240 value 206.941389
## iter 250 value 203.539263
## iter 260 value 201.824722
## iter 270 value 199.461087
## iter 280 value 197.808906
## iter 290 value 196.920781
## iter 300 value 196.555254
## iter 310 value 195.844090
## iter 320 value 194.231968
## iter 330 value 193.128309
## iter 340 value 191.420532
## iter 350 value 188.587819
## iter 360 value 184.520386
## iter 370 value 179.776055
## iter 380 value 175.859136
## iter 390 value 171.486382
## iter 400 value 166.632662
## iter 410 value 163.699258
## iter 420 value 161.636128
## iter 430 value 157.573503
## iter 440 value 153.444373
## iter 450 value 149.425640
## iter 460 value 146.048195
## iter 470 value 143.776179
## iter 480 value 142.624020
## iter 490 value 141.905486
## iter 500 value 141.439992
## final value 141.439992
## stopped after 500 iterations
## # weights: 15
## initial value 1412123.848788
## iter 10 value 17816.127669
## iter 20 value 4213.296761
## iter 30 value 3129.584344
## iter 40 value 2501.798233
## iter 50 value 2195.138494
## iter 60 value 1954.790678
## iter 70 value 1732.478504
## iter 80 value 1693.816072
## iter 90 value 1633.072117
## iter 100 value 1614.261929
## iter 110 value 1612.677947
## iter 120 value 1612.338431
## final value 1612.338009
## converged
## # weights: 36
## initial value 1431181.779951
## iter 10 value 9705.906100
## iter 20 value 7564.749401
## iter 30 value 6574.719690
## iter 40 value 3897.015955
## iter 50 value 2875.560804
## iter 60 value 2099.440875
## iter 70 value 1552.504797
## iter 80 value 1386.601634
## iter 90 value 1340.352030
## iter 100 value 1312.223614
## iter 110 value 1303.925527
## iter 120 value 1299.296974
## iter 130 value 1295.033185
## iter 140 value 1289.054234
## iter 150 value 1271.629045
## iter 160 value 1241.356303
## iter 170 value 1188.991495
## iter 180 value 1162.878653
## iter 190 value 1158.003381
## iter 200 value 1157.145021
## iter 210 value 1156.248857
## iter 220 value 1155.900267
## final value 1155.889083
## converged
## # weights: 71
## initial value 1403758.018815
## iter 10 value 2486.954813
## iter 20 value 1678.962782
## iter 30 value 1483.676972
## iter 40 value 1403.205838
## iter 50 value 1353.589959
## iter 60 value 1312.588254
## iter 70 value 1284.904676
## iter 80 value 1261.197500
## iter 90 value 1227.126801
## iter 100 value 1170.160491
## iter 110 value 1130.443836
## iter 120 value 1099.530558
## iter 130 value 1078.209711
## iter 140 value 1052.069029
## iter 150 value 1024.489084
## iter 160 value 1014.269621
## iter 170 value 997.177742
## iter 180 value 974.741669
## iter 190 value 951.376296
## iter 200 value 937.397438
## iter 210 value 927.014057
## iter 220 value 920.805077
## iter 230 value 911.812329
## iter 240 value 904.520730
## iter 250 value 901.543821
## iter 260 value 899.694674
## iter 270 value 898.236304
## iter 280 value 897.681775
## iter 290 value 897.239717
## iter 300 value 897.186828
## iter 310 value 897.098651
## iter 320 value 897.038988
## iter 330 value 896.982875
## iter 340 value 896.955409
## iter 350 value 896.944911
## final value 896.944785
## converged
## # weights: 106
## initial value 1382041.950975
## iter 10 value 1384.000628
## iter 20 value 1101.128149
## iter 30 value 987.671665
## iter 40 value 912.754629
## iter 50 value 858.698285
## iter 60 value 833.833243
## iter 70 value 799.096292
## iter 80 value 761.899846
## iter 90 value 736.607653
## iter 100 value 716.283311
## iter 110 value 701.648636
## iter 120 value 690.761524
## iter 130 value 676.008617
## iter 140 value 666.765224
## iter 150 value 661.799875
## iter 160 value 655.446013
## iter 170 value 650.198377
## iter 180 value 644.866209
## iter 190 value 638.226034
## iter 200 value 632.019667
## iter 210 value 628.095028
## iter 220 value 626.836556
## iter 230 value 625.387357
## iter 240 value 623.240263
## iter 250 value 621.699562
## iter 260 value 620.555860
## iter 270 value 619.360963
## iter 280 value 618.021215
## iter 290 value 617.169069
## iter 300 value 616.029885
## iter 310 value 614.909341
## iter 320 value 613.597784
## iter 330 value 611.404882
## iter 340 value 610.353821
## iter 350 value 609.832772
## iter 360 value 609.447249
## iter 370 value 609.308032
## iter 380 value 609.237639
## iter 390 value 609.180906
## iter 400 value 609.158370
## iter 410 value 609.139588
## iter 420 value 609.138905
## final value 609.138891
## converged
## # weights: 141
## initial value 1385092.616293
## iter 10 value 1948.394252
## iter 20 value 1232.669851
## iter 30 value 1102.571705
## iter 40 value 982.767266
## iter 50 value 875.009156
## iter 60 value 810.373744
## iter 70 value 777.303965
## iter 80 value 747.839620
## iter 90 value 726.065221
## iter 100 value 707.276226
## iter 110 value 695.120362
## iter 120 value 679.130091
## iter 130 value 659.575796
## iter 140 value 644.640796
## iter 150 value 637.986588
## iter 160 value 632.960017
## iter 170 value 628.043587
## iter 180 value 619.653308
## iter 190 value 615.223589
## iter 200 value 612.360386
## iter 210 value 610.238602
## iter 220 value 608.340191
## iter 230 value 605.428699
## iter 240 value 602.297260
## iter 250 value 599.588594
## iter 260 value 597.787583
## iter 270 value 595.135891
## iter 280 value 585.022136
## iter 290 value 575.466559
## iter 300 value 570.159954
## iter 310 value 562.084254
## iter 320 value 552.930719
## iter 330 value 545.582173
## iter 340 value 540.122187
## iter 350 value 535.265294
## iter 360 value 532.628995
## iter 370 value 530.071624
## iter 380 value 528.060579
## iter 390 value 526.262586
## iter 400 value 525.339962
## iter 410 value 524.892119
## iter 420 value 524.023242
## iter 430 value 522.718460
## iter 440 value 522.069341
## iter 450 value 521.529123
## iter 460 value 521.346000
## iter 470 value 521.324263
## iter 480 value 521.314622
## iter 490 value 521.309971
## iter 500 value 521.307539
## final value 521.307539
## stopped after 500 iterations
## # weights: 15
## initial value 1432508.485107
## iter 10 value 5339.346468
## iter 20 value 3559.675302
## iter 30 value 2226.302987
## iter 40 value 1816.054506
## iter 50 value 1775.609031
## iter 60 value 1743.468383
## iter 70 value 1735.108816
## iter 80 value 1730.896427
## iter 90 value 1641.715324
## iter 100 value 1501.577674
## iter 110 value 1427.786525
## iter 120 value 1373.888751
## iter 130 value 1267.663270
## iter 140 value 1187.856222
## iter 150 value 1180.076137
## iter 160 value 1172.141917
## iter 170 value 1167.677887
## iter 180 value 1167.273107
## iter 190 value 1165.696008
## iter 200 value 1164.762732
## iter 210 value 1164.669219
## iter 220 value 1164.615670
## iter 230 value 1164.592541
## final value 1164.591750
## converged
## # weights: 36
## initial value 1388708.885180
## iter 10 value 5973.882990
## iter 20 value 4403.024030
## iter 30 value 3904.147124
## iter 40 value 3212.204664
## iter 50 value 2719.799176
## iter 60 value 2223.515068
## iter 70 value 2096.897718
## iter 80 value 1989.025444
## iter 90 value 1904.602705
## iter 100 value 1474.363221
## iter 110 value 1399.616047
## iter 120 value 1350.869603
## iter 130 value 1317.414053
## iter 140 value 1279.222464
## iter 150 value 1260.784377
## iter 160 value 1245.351813
## iter 170 value 1182.713357
## iter 180 value 1159.840787
## iter 190 value 1122.465503
## iter 200 value 1097.101789
## iter 210 value 1088.784953
## iter 220 value 1080.037009
## iter 230 value 1074.656202
## iter 240 value 1051.272103
## iter 250 value 1011.099836
## iter 260 value 998.719834
## iter 270 value 992.754856
## iter 280 value 970.061953
## iter 290 value 963.994104
## iter 300 value 958.076888
## iter 310 value 957.154108
## iter 320 value 956.992009
## final value 956.960470
## converged
## # weights: 71
## initial value 1414060.565511
## iter 10 value 1470.441608
## iter 20 value 1140.396857
## iter 30 value 1005.112034
## iter 40 value 944.503247
## iter 50 value 879.403398
## iter 60 value 830.461347
## iter 70 value 766.620792
## iter 80 value 725.179608
## iter 90 value 703.923690
## iter 100 value 686.921141
## iter 110 value 681.456410
## iter 120 value 671.277401
## iter 130 value 663.666234
## iter 140 value 657.604683
## iter 150 value 651.569128
## iter 160 value 648.109202
## iter 170 value 642.751676
## iter 180 value 637.114922
## iter 190 value 625.724848
## iter 200 value 618.017962
## iter 210 value 601.061879
## iter 220 value 586.780671
## iter 230 value 577.647206
## iter 240 value 572.822859
## iter 250 value 570.184695
## iter 260 value 567.304796
## iter 270 value 565.373935
## iter 280 value 562.558281
## iter 290 value 554.774704
## iter 300 value 554.106164
## iter 310 value 552.564583
## iter 320 value 551.050081
## iter 330 value 549.565636
## iter 340 value 547.767807
## iter 350 value 547.108280
## iter 360 value 546.821199
## iter 370 value 546.703356
## iter 380 value 546.650465
## iter 390 value 546.637810
## iter 400 value 546.636447
## final value 546.636159
## converged
## # weights: 106
## initial value 1417058.631998
## iter 10 value 1460.257960
## iter 20 value 1093.108040
## iter 30 value 968.028515
## iter 40 value 888.312314
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## iter 390 value 398.788796
## iter 400 value 398.786717
## iter 410 value 398.786553
## final value 398.786536
## converged
## # weights: 141
## initial value 1355818.693137
## iter 10 value 1360.133812
## iter 20 value 984.913011
## iter 30 value 845.216891
## iter 40 value 768.562998
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## iter 250 value 273.471175
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## iter 470 value 223.863862
## iter 480 value 222.110833
## iter 490 value 220.282719
## iter 500 value 218.407044
## final value 218.407044
## stopped after 500 iterations
## # weights: 15
## initial value 1389878.342298
## iter 10 value 11181.065855
## iter 20 value 4652.209575
## iter 30 value 1886.737413
## iter 40 value 1832.986654
## iter 50 value 1687.531635
## iter 60 value 1634.128802
## iter 70 value 1569.450801
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## iter 180 value 1263.141703
## iter 190 value 1211.903138
## iter 200 value 1191.422154
## iter 210 value 1160.507839
## final value 1159.227722
## converged
## # weights: 36
## initial value 1379452.138405
## iter 10 value 470956.100085
## iter 20 value 6956.222637
## iter 30 value 5359.511024
## iter 40 value 4540.502955
## iter 50 value 3698.589962
## iter 60 value 2777.832924
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## iter 250 value 1488.396689
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## iter 320 value 1438.058631
## iter 330 value 1437.845194
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## iter 470 value 1166.562136
## iter 480 value 1123.299296
## iter 490 value 1090.519466
## iter 500 value 1075.379096
## final value 1075.379096
## stopped after 500 iterations
## # weights: 71
## initial value 1404574.992625
## iter 10 value 2497.312276
## iter 20 value 1689.574488
## iter 30 value 1259.310928
## iter 40 value 1128.798406
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## iter 470 value 656.063977
## iter 480 value 642.541912
## iter 490 value 640.109619
## iter 500 value 637.287794
## final value 637.287794
## stopped after 500 iterations
## # weights: 106
## initial value 1466744.576828
## iter 10 value 1949.525647
## iter 20 value 1198.145707
## iter 30 value 961.891397
## iter 40 value 847.673786
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## final value 270.698108
## stopped after 500 iterations
## # weights: 141
## initial value 1374891.404402
## iter 10 value 1386.133672
## iter 20 value 1071.423448
## iter 30 value 886.405820
## iter 40 value 788.568656
## iter 50 value 713.049041
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## final value 201.815602
## stopped after 500 iterations
## # weights: 15
## initial value 1420774.165213
## iter 10 value 6414.129254
## iter 20 value 5813.352589
## iter 30 value 4862.960337
## iter 40 value 3268.292716
## iter 50 value 2462.536656
## iter 60 value 1611.251157
## iter 70 value 1370.345345
## iter 80 value 1342.294010
## iter 90 value 1285.429562
## iter 100 value 1224.400646
## iter 110 value 1209.957789
## iter 120 value 1175.414182
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## iter 160 value 1158.898149
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## iter 200 value 1157.544140
## iter 210 value 1157.537344
## iter 220 value 1157.391520
## iter 230 value 1157.294973
## final value 1157.294902
## converged
## # weights: 36
## initial value 1417826.813888
## iter 10 value 5918.596206
## iter 20 value 3038.662120
## iter 30 value 1932.067251
## iter 40 value 1392.560625
## iter 50 value 1254.338043
## iter 60 value 1198.402287
## iter 70 value 1177.932116
## iter 80 value 1171.554781
## iter 90 value 1161.518735
## iter 100 value 1150.224216
## iter 110 value 1119.589898
## iter 120 value 1092.283455
## iter 130 value 1080.965825
## iter 140 value 1080.146157
## iter 150 value 1079.263465
## iter 160 value 1078.992786
## iter 170 value 1078.980893
## iter 180 value 1078.896782
## iter 180 value 1078.896771
## final value 1078.896771
## converged
## # weights: 71
## initial value 1415081.982814
## iter 10 value 3553.481708
## iter 20 value 2218.502935
## iter 30 value 1404.725219
## iter 40 value 1145.963674
## iter 50 value 995.315998
## iter 60 value 945.009644
## iter 70 value 914.582198
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## iter 270 value 576.650362
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## iter 300 value 564.307827
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## iter 500 value 544.842008
## final value 544.842008
## stopped after 500 iterations
## # weights: 106
## initial value 1439929.964485
## iter 10 value 1552.564060
## iter 20 value 1034.583771
## iter 30 value 895.904341
## iter 40 value 823.484717
## iter 50 value 785.700622
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## iter 300 value 377.726577
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## iter 470 value 335.416426
## iter 480 value 335.180318
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## iter 500 value 334.906565
## final value 334.906565
## stopped after 500 iterations
## # weights: 141
## initial value 1429753.188883
## iter 10 value 1845.074529
## iter 20 value 1256.776382
## iter 30 value 974.823715
## iter 40 value 794.899919
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## iter 500 value 221.511772
## final value 221.511772
## stopped after 500 iterations
## # weights: 15
## initial value 1425529.190665
## iter 10 value 4416.080235
## iter 20 value 2580.906070
## iter 30 value 2013.762357
## iter 40 value 1756.856814
## iter 50 value 1567.975340
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## iter 350 value 1390.280897
## iter 360 value 1388.864965
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## iter 470 value 1379.374598
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## iter 490 value 1377.977934
## iter 500 value 1377.628995
## final value 1377.628995
## stopped after 500 iterations
## # weights: 36
## initial value 1372062.450316
## iter 10 value 3559.658349
## iter 20 value 2090.046609
## iter 30 value 1731.799937
## iter 40 value 1344.075155
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## iter 80 value 927.231012
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## final value 771.678847
## stopped after 500 iterations
## # weights: 71
## initial value 1407405.516296
## iter 10 value 1285.530881
## iter 20 value 1081.169193
## iter 30 value 1005.220023
## iter 40 value 968.939347
## iter 50 value 913.203922
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## final value 424.058395
## stopped after 500 iterations
## # weights: 106
## initial value 1424814.260133
## iter 10 value 1594.538846
## iter 20 value 1102.098337
## iter 30 value 991.671078
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## iter 370 value 307.190392
## iter 380 value 306.120662
## iter 390 value 304.759661
## iter 400 value 303.247248
## iter 410 value 301.866282
## iter 420 value 301.447400
## iter 430 value 301.216995
## iter 440 value 301.173436
## iter 450 value 301.102476
## iter 460 value 301.025853
## iter 470 value 300.919934
## iter 480 value 300.843856
## iter 490 value 300.785988
## iter 500 value 300.743492
## final value 300.743492
## stopped after 500 iterations
## # weights: 141
## initial value 1374718.962443
## iter 10 value 3867.947258
## iter 20 value 1181.418014
## iter 30 value 917.745772
## iter 40 value 730.277187
## iter 50 value 629.976521
## iter 60 value 560.904120
## iter 70 value 508.384994
## iter 80 value 479.468314
## iter 90 value 455.683099
## iter 100 value 433.409400
## iter 110 value 406.792641
## iter 120 value 389.212414
## iter 130 value 370.770323
## iter 140 value 360.463879
## iter 150 value 352.582510
## iter 160 value 347.151858
## iter 170 value 342.584044
## iter 180 value 338.536125
## iter 190 value 333.363048
## iter 200 value 331.222135
## iter 210 value 328.358136
## iter 220 value 325.539217
## iter 230 value 321.973662
## iter 240 value 316.170390
## iter 250 value 309.502115
## iter 260 value 301.747857
## iter 270 value 297.624084
## iter 280 value 294.009404
## iter 290 value 292.743939
## iter 300 value 292.161368
## iter 310 value 291.300523
## iter 320 value 289.169547
## iter 330 value 287.357796
## iter 340 value 285.450748
## iter 350 value 282.538819
## iter 360 value 279.754132
## iter 370 value 274.362949
## iter 380 value 270.195928
## iter 390 value 265.590122
## iter 400 value 261.111312
## iter 410 value 255.563835
## iter 420 value 250.297615
## iter 430 value 246.809215
## iter 440 value 244.839308
## iter 450 value 243.157614
## iter 460 value 241.861797
## iter 470 value 240.940082
## iter 480 value 240.353628
## iter 490 value 239.881087
## iter 500 value 239.169566
## final value 239.169566
## stopped after 500 iterations
## # weights: 15
## initial value 1431873.437992
## iter 10 value 11878.146060
## iter 20 value 6578.310020
## iter 30 value 5704.810366
## iter 40 value 3808.295340
## iter 50 value 2442.939464
## iter 60 value 1776.195343
## iter 70 value 1579.653803
## iter 80 value 1495.946338
## iter 90 value 1473.417703
## iter 100 value 1416.139657
## iter 110 value 1396.739612
## iter 120 value 1394.396687
## iter 130 value 1394.189830
## final value 1394.189776
## converged
## # weights: 36
## initial value 1430331.198013
## iter 10 value 18676.235133
## iter 20 value 9513.110260
## iter 30 value 6750.324980
## iter 40 value 4806.755940
## iter 50 value 3614.906157
## iter 60 value 2645.032578
## iter 70 value 2233.237310
## iter 80 value 1948.274621
## iter 90 value 1765.058969
## iter 100 value 1620.401578
## iter 110 value 1431.533548
## iter 120 value 1344.381896
## iter 130 value 1295.481241
## iter 140 value 1260.349687
## iter 150 value 1234.615332
## iter 160 value 1215.679553
## iter 170 value 1198.309315
## iter 180 value 1155.842502
## iter 190 value 1134.255219
## iter 200 value 1125.618284
## iter 210 value 1124.785430
## iter 220 value 1124.672863
## iter 230 value 1124.632976
## iter 240 value 1124.590404
## iter 250 value 1124.495576
## iter 260 value 1124.429942
## final value 1124.429772
## converged
## # weights: 71
## initial value 1368000.667055
## iter 10 value 1850.053102
## iter 20 value 1265.692526
## iter 30 value 1171.863894
## iter 40 value 1118.397806
## iter 50 value 1080.917850
## iter 60 value 1036.436256
## iter 70 value 1009.061296
## iter 80 value 987.113551
## iter 90 value 971.971624
## iter 100 value 959.849128
## iter 110 value 949.056143
## iter 120 value 935.716173
## iter 130 value 926.788659
## iter 140 value 919.760793
## iter 150 value 915.049127
## iter 160 value 910.306834
## iter 170 value 903.357062
## iter 180 value 896.366872
## iter 190 value 893.761901
## iter 200 value 892.293931
## iter 210 value 890.109988
## iter 220 value 884.053138
## iter 230 value 880.127699
## iter 240 value 879.424549
## iter 250 value 879.231307
## iter 260 value 879.217898
## final value 879.217409
## converged
## # weights: 106
## initial value 1351450.994197
## iter 10 value 1383.054146
## iter 20 value 1147.076492
## iter 30 value 1023.484655
## iter 40 value 929.947252
## iter 50 value 877.216886
## iter 60 value 848.801645
## iter 70 value 810.846709
## iter 80 value 781.267550
## iter 90 value 763.936016
## iter 100 value 754.845719
## iter 110 value 732.981916
## iter 120 value 695.279141
## iter 130 value 674.587016
## iter 140 value 664.015421
## iter 150 value 658.033951
## iter 160 value 652.472670
## iter 170 value 646.477523
## iter 180 value 643.892102
## iter 190 value 638.846179
## iter 200 value 633.025087
## iter 210 value 631.224742
## iter 220 value 629.418101
## iter 230 value 627.069737
## iter 240 value 623.763129
## iter 250 value 621.789205
## iter 260 value 620.498994
## iter 270 value 619.723572
## iter 280 value 619.323303
## iter 290 value 619.022169
## iter 300 value 618.753879
## iter 310 value 618.635870
## iter 320 value 618.611610
## final value 618.605267
## converged
## # weights: 141
## initial value 1414573.195862
## iter 10 value 1738.543625
## iter 20 value 1175.618192
## iter 30 value 1038.700648
## iter 40 value 987.785092
## iter 50 value 913.653123
## iter 60 value 855.534047
## iter 70 value 810.916695
## iter 80 value 779.537571
## iter 90 value 748.333623
## iter 100 value 720.801955
## iter 110 value 697.199757
## iter 120 value 680.627682
## iter 130 value 665.197338
## iter 140 value 648.901316
## iter 150 value 631.947465
## iter 160 value 623.766745
## iter 170 value 617.762456
## iter 180 value 610.083020
## iter 190 value 598.965711
## iter 200 value 589.640915
## iter 210 value 584.270266
## iter 220 value 581.519352
## iter 230 value 579.075103
## iter 240 value 577.163564
## iter 250 value 576.039855
## iter 260 value 574.916167
## iter 270 value 572.315351
## iter 280 value 569.811148
## iter 290 value 568.816832
## iter 300 value 567.639118
## iter 310 value 565.983362
## iter 320 value 563.903763
## iter 330 value 561.236246
## iter 340 value 557.659080
## iter 350 value 553.892787
## iter 360 value 549.182778
## iter 370 value 540.612877
## iter 380 value 534.731396
## iter 390 value 530.054984
## iter 400 value 525.767070
## iter 410 value 523.294809
## iter 420 value 519.093696
## iter 430 value 516.317632
## iter 440 value 515.010576
## iter 450 value 514.337925
## iter 460 value 513.968352
## iter 470 value 513.805914
## iter 480 value 513.767460
## iter 490 value 513.752568
## iter 500 value 513.748522
## final value 513.748522
## stopped after 500 iterations
## # weights: 15
## initial value 1389376.583573
## iter 10 value 42513.885861
## iter 20 value 7760.089986
## iter 30 value 4647.309785
## iter 40 value 2821.511176
## iter 50 value 1697.598772
## iter 60 value 1416.596018
## iter 70 value 1350.818268
## iter 80 value 1264.669605
## iter 90 value 1194.266874
## iter 100 value 1165.181000
## iter 110 value 1143.335251
## iter 120 value 1128.393794
## iter 130 value 1124.758777
## iter 140 value 1123.639926
## iter 150 value 1121.896772
## iter 160 value 1121.493406
## iter 170 value 1121.321700
## iter 180 value 1121.142484
## final value 1121.130155
## converged
## # weights: 36
## initial value 1426640.918311
## iter 10 value 305748.306450
## iter 20 value 201935.940094
## iter 30 value 7851.023625
## iter 40 value 5881.046265
## iter 50 value 5626.968655
## iter 60 value 5443.310648
## iter 70 value 3891.670765
## iter 80 value 3058.910714
## iter 90 value 2519.763894
## iter 100 value 2010.773381
## iter 110 value 1834.282124
## iter 120 value 1707.739772
## iter 130 value 1439.368209
## iter 140 value 1398.847167
## iter 150 value 1316.421473
## iter 160 value 1297.961909
## iter 170 value 1205.379355
## iter 180 value 1144.416753
## iter 190 value 1103.867625
## iter 200 value 1080.196190
## iter 210 value 1045.569477
## iter 220 value 1030.168361
## iter 230 value 1027.902224
## iter 240 value 1024.874845
## iter 250 value 1018.938487
## iter 260 value 1009.808285
## iter 270 value 997.216460
## iter 280 value 966.500811
## iter 290 value 938.936122
## iter 300 value 930.124601
## iter 310 value 924.429835
## iter 320 value 918.416532
## iter 330 value 912.237135
## iter 340 value 907.474656
## iter 350 value 906.090403
## iter 360 value 904.053334
## iter 370 value 903.172606
## iter 380 value 903.021779
## iter 390 value 902.756015
## iter 400 value 902.220670
## iter 410 value 902.100105
## iter 420 value 902.080627
## iter 430 value 902.075992
## final value 902.075688
## converged
## # weights: 71
## initial value 1394268.565642
## iter 10 value 1873.712711
## iter 20 value 1317.003912
## iter 30 value 1094.609181
## iter 40 value 999.750983
## iter 50 value 937.663120
## iter 60 value 906.037934
## iter 70 value 872.826445
## iter 80 value 844.494706
## iter 90 value 803.824136
## iter 100 value 748.359752
## iter 110 value 738.653201
## iter 120 value 721.366946
## iter 130 value 705.023144
## iter 140 value 698.711951
## iter 150 value 697.537037
## iter 160 value 697.010131
## iter 170 value 694.681049
## iter 180 value 688.978444
## iter 190 value 682.577655
## iter 200 value 678.197417
## iter 210 value 670.835703
## iter 220 value 664.482069
## iter 230 value 660.525414
## iter 240 value 657.694948
## iter 250 value 654.533808
## iter 260 value 652.340445
## iter 270 value 650.125552
## iter 280 value 649.415338
## iter 290 value 649.246118
## iter 300 value 649.204906
## iter 310 value 649.168164
## iter 320 value 649.116135
## iter 330 value 649.026350
## iter 340 value 648.852544
## iter 350 value 648.744976
## iter 360 value 648.708102
## iter 370 value 648.675190
## iter 380 value 648.670736
## iter 390 value 648.669079
## final value 648.668996
## converged
## # weights: 106
## initial value 1417638.523158
## iter 10 value 1795.865423
## iter 20 value 1006.698836
## iter 30 value 879.507381
## iter 40 value 799.718596
## iter 50 value 747.307981
## iter 60 value 716.944440
## iter 70 value 651.785899
## iter 80 value 591.141017
## iter 90 value 559.055881
## iter 100 value 521.956807
## iter 110 value 485.087138
## iter 120 value 459.083686
## iter 130 value 427.732516
## iter 140 value 411.810075
## iter 150 value 394.458348
## iter 160 value 384.757527
## iter 170 value 372.763265
## iter 180 value 362.352760
## iter 190 value 346.996447
## iter 200 value 340.624309
## iter 210 value 335.872534
## iter 220 value 333.068989
## iter 230 value 330.730462
## iter 240 value 327.965220
## iter 250 value 323.747396
## iter 260 value 317.945625
## iter 270 value 314.161351
## iter 280 value 310.872620
## iter 290 value 306.041003
## iter 300 value 300.063982
## iter 310 value 297.073617
## iter 320 value 295.519161
## iter 330 value 294.576755
## iter 340 value 294.161286
## iter 350 value 294.007460
## iter 360 value 293.947444
## iter 370 value 293.680345
## iter 380 value 292.722926
## iter 390 value 291.119413
## iter 400 value 290.113679
## iter 410 value 289.913108
## iter 420 value 289.873115
## iter 430 value 289.867138
## iter 440 value 289.866708
## iter 450 value 289.865698
## iter 460 value 289.863793
## iter 470 value 289.860844
## iter 480 value 289.858005
## iter 490 value 289.855406
## iter 500 value 289.853558
## final value 289.853558
## stopped after 500 iterations
## # weights: 141
## initial value 1397917.031843
## iter 10 value 1359.837703
## iter 20 value 1056.886160
## iter 30 value 888.798096
## iter 40 value 816.433030
## iter 50 value 720.584238
## iter 60 value 617.674906
## iter 70 value 524.957506
## iter 80 value 486.309386
## iter 90 value 444.959992
## iter 100 value 421.098888
## iter 110 value 386.121525
## iter 120 value 351.675891
## iter 130 value 336.895899
## iter 140 value 323.907115
## iter 150 value 308.911057
## iter 160 value 290.344204
## iter 170 value 277.100566
## iter 180 value 268.845961
## iter 190 value 261.411070
## iter 200 value 254.461326
## iter 210 value 245.373395
## iter 220 value 237.259665
## iter 230 value 231.892659
## iter 240 value 228.379697
## iter 250 value 225.165446
## iter 260 value 222.501844
## iter 270 value 219.469101
## iter 280 value 217.870288
## iter 290 value 217.446960
## iter 300 value 217.110444
## iter 310 value 216.729673
## iter 320 value 216.115111
## iter 330 value 215.349188
## iter 340 value 214.093226
## iter 350 value 212.505758
## iter 360 value 211.019023
## iter 370 value 209.027055
## iter 380 value 207.599435
## iter 390 value 205.898556
## iter 400 value 204.266096
## iter 410 value 202.879831
## iter 420 value 201.225020
## iter 430 value 199.766989
## iter 440 value 197.682816
## iter 450 value 196.172051
## iter 460 value 194.850052
## iter 470 value 193.808291
## iter 480 value 193.203684
## iter 490 value 192.699709
## iter 500 value 192.429809
## final value 192.429809
## stopped after 500 iterations
## # weights: 15
## initial value 1418647.988848
## iter 10 value 7802.726941
## iter 20 value 3190.461116
## iter 30 value 1701.366337
## iter 40 value 1555.977145
## iter 50 value 1471.532427
## iter 60 value 1404.936526
## iter 70 value 1201.489434
## iter 80 value 1140.062653
## iter 90 value 1134.717964
## iter 100 value 1124.395355
## iter 110 value 1121.952263
## iter 120 value 1121.358112
## iter 130 value 1119.606285
## iter 140 value 1118.254126
## iter 150 value 1118.197345
## iter 160 value 1117.807172
## iter 170 value 1116.931237
## iter 180 value 1116.921685
## iter 190 value 1116.867487
## iter 200 value 1116.533878
## iter 210 value 1116.510917
## iter 220 value 1116.493598
## iter 230 value 1116.446357
## final value 1116.445325
## converged
## # weights: 36
## initial value 1427170.219825
## iter 10 value 39071.045249
## iter 20 value 9279.542911
## iter 30 value 2874.803980
## iter 40 value 1736.277200
## iter 50 value 1497.026126
## iter 60 value 1373.409921
## iter 70 value 1339.471009
## iter 80 value 1313.762026
## iter 90 value 1261.645624
## iter 100 value 1250.490423
## iter 110 value 1245.566963
## iter 120 value 1241.579924
## iter 130 value 1239.196521
## iter 140 value 1217.123366
## iter 150 value 1174.713316
## iter 160 value 1167.636967
## iter 170 value 1161.038392
## iter 180 value 1156.729875
## iter 190 value 1155.155791
## iter 200 value 1149.459696
## iter 210 value 1148.276345
## iter 220 value 1145.858745
## iter 230 value 1145.453377
## iter 240 value 1144.060618
## iter 250 value 1143.535749
## iter 260 value 1143.404843
## iter 270 value 1141.279029
## iter 280 value 1121.017612
## iter 290 value 1087.147054
## iter 300 value 1066.275535
## iter 310 value 1055.023777
## iter 320 value 1049.716266
## iter 330 value 1046.276794
## iter 340 value 1042.206722
## iter 350 value 1031.494478
## iter 360 value 1025.534778
## iter 370 value 1021.870643
## iter 380 value 1019.831706
## iter 390 value 1018.671908
## iter 400 value 1018.144157
## iter 410 value 1017.944022
## iter 420 value 1017.693434
## iter 430 value 1017.633236
## iter 440 value 1017.506815
## iter 450 value 1017.309112
## iter 460 value 1017.286308
## iter 470 value 1017.268432
## iter 470 value 1017.268426
## final value 1017.268426
## converged
## # weights: 71
## initial value 1426719.876281
## iter 10 value 1783.190871
## iter 20 value 1114.935635
## iter 30 value 992.551670
## iter 40 value 917.560367
## iter 50 value 857.733813
## iter 60 value 821.912889
## iter 70 value 800.687860
## iter 80 value 789.036687
## iter 90 value 782.639580
## iter 100 value 770.919928
## iter 110 value 764.131194
## iter 120 value 758.322088
## iter 130 value 755.538800
## iter 140 value 754.511775
## iter 150 value 753.983869
## iter 160 value 753.516688
## iter 170 value 752.056473
## iter 180 value 748.354181
## iter 190 value 746.074462
## iter 200 value 740.436190
## iter 210 value 729.813844
## iter 220 value 723.273467
## iter 230 value 719.326572
## iter 240 value 710.953646
## iter 250 value 704.119830
## iter 260 value 698.136399
## iter 270 value 695.641047
## iter 280 value 692.206845
## iter 290 value 689.423838
## iter 300 value 688.609514
## iter 310 value 687.874940
## iter 320 value 686.510198
## iter 330 value 686.074217
## iter 340 value 685.927384
## iter 350 value 685.658303
## iter 360 value 685.486237
## iter 370 value 684.585305
## iter 380 value 682.903028
## iter 390 value 682.160421
## iter 400 value 681.188697
## iter 410 value 680.507383
## iter 420 value 680.214694
## iter 430 value 680.185311
## iter 440 value 680.061735
## iter 450 value 680.041336
## iter 460 value 680.034540
## iter 460 value 680.034534
## iter 460 value 680.034532
## final value 680.034532
## converged
## # weights: 106
## initial value 1381374.794335
## iter 10 value 1299.734265
## iter 20 value 1056.887523
## iter 30 value 924.246473
## iter 40 value 820.543008
## iter 50 value 771.663785
## iter 60 value 708.455744
## iter 70 value 663.519686
## iter 80 value 636.717381
## iter 90 value 608.681548
## iter 100 value 597.299079
## iter 110 value 574.245431
## iter 120 value 537.280028
## iter 130 value 512.425534
## iter 140 value 490.554618
## iter 150 value 462.802398
## iter 160 value 442.416228
## iter 170 value 422.548174
## iter 180 value 411.453751
## iter 190 value 400.732569
## iter 200 value 393.325163
## iter 210 value 388.684376
## iter 220 value 387.294850
## iter 230 value 385.792397
## iter 240 value 383.828614
## iter 250 value 379.613521
## iter 260 value 375.572724
## iter 270 value 367.784379
## iter 280 value 358.778228
## iter 290 value 352.815450
## iter 300 value 348.352702
## iter 310 value 341.713739
## iter 320 value 337.311272
## iter 330 value 333.400939
## iter 340 value 329.210252
## iter 350 value 326.817503
## iter 360 value 324.349044
## iter 370 value 322.528903
## iter 380 value 312.804611
## iter 390 value 309.146648
## iter 400 value 305.206635
## iter 410 value 303.031465
## iter 420 value 301.705990
## iter 430 value 301.186973
## iter 440 value 301.102088
## iter 450 value 300.964212
## iter 460 value 300.765090
## iter 470 value 300.643788
## iter 480 value 300.526312
## iter 490 value 300.414483
## iter 500 value 300.309006
## final value 300.309006
## stopped after 500 iterations
## # weights: 141
## initial value 1348649.262086
## iter 10 value 1797.641927
## iter 20 value 1103.834976
## iter 30 value 959.107228
## iter 40 value 863.552441
## iter 50 value 777.611731
## iter 60 value 688.022225
## iter 70 value 600.893262
## iter 80 value 546.384911
## iter 90 value 493.258586
## iter 100 value 445.222399
## iter 110 value 416.494274
## iter 120 value 397.464881
## iter 130 value 385.392480
## iter 140 value 371.794476
## iter 150 value 355.871300
## iter 160 value 334.104825
## iter 170 value 311.003225
## iter 180 value 298.266561
## iter 190 value 288.192667
## iter 200 value 279.541700
## iter 210 value 270.401998
## iter 220 value 262.201877
## iter 230 value 251.713556
## iter 240 value 246.065889
## iter 250 value 241.961546
## iter 260 value 239.441350
## iter 270 value 237.927797
## iter 280 value 236.833032
## iter 290 value 236.402665
## iter 300 value 235.956323
## iter 310 value 234.896134
## iter 320 value 233.127371
## iter 330 value 231.783961
## iter 340 value 229.847097
## iter 350 value 226.199324
## iter 360 value 223.951083
## iter 370 value 221.235639
## iter 380 value 218.266239
## iter 390 value 215.617201
## iter 400 value 214.288562
## iter 410 value 212.057061
## iter 420 value 210.885106
## iter 430 value 208.335861
## iter 440 value 206.301861
## iter 450 value 205.231426
## iter 460 value 204.470067
## iter 470 value 203.790337
## iter 480 value 203.617950
## iter 490 value 203.526646
## iter 500 value 203.453954
## final value 203.453954
## stopped after 500 iterations
## # weights: 15
## initial value 1437213.302387
## iter 10 value 5255.207331
## iter 20 value 3949.388728
## iter 30 value 2290.690213
## iter 40 value 1836.722757
## iter 50 value 1770.770430
## iter 60 value 1751.021590
## iter 70 value 1739.954030
## iter 80 value 1736.133683
## iter 90 value 1735.518402
## iter 90 value 1735.518397
## final value 1735.517841
## converged
## # weights: 36
## initial value 1403685.143634
## iter 10 value 7291.596634
## iter 20 value 3345.667307
## iter 30 value 2854.230526
## iter 40 value 2735.652249
## iter 50 value 2662.486507
## iter 60 value 2568.819529
## iter 70 value 2219.920956
## iter 80 value 1980.937463
## iter 90 value 1679.683030
## iter 100 value 1563.678027
## iter 110 value 1545.352535
## iter 120 value 1526.578521
## iter 130 value 1515.343169
## iter 140 value 1506.192877
## iter 150 value 1498.975117
## iter 160 value 1490.369460
## iter 170 value 1487.801718
## iter 180 value 1485.897615
## iter 190 value 1485.775181
## iter 200 value 1485.650531
## iter 210 value 1485.501996
## iter 220 value 1485.497904
## iter 230 value 1485.484835
## final value 1485.484739
## converged
## # weights: 71
## initial value 1371779.205282
## iter 10 value 2041.259453
## iter 20 value 1180.730791
## iter 30 value 965.307145
## iter 40 value 896.110973
## iter 50 value 853.485688
## iter 60 value 830.740975
## iter 70 value 786.858172
## iter 80 value 758.653251
## iter 90 value 736.067309
## iter 100 value 696.525942
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## final value 529.176818
## stopped after 500 iterations
## # weights: 106
## initial value 1389419.598289
## iter 10 value 1864.740434
## iter 20 value 1103.814917
## iter 30 value 899.655533
## iter 40 value 826.906983
## iter 50 value 782.069824
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## iter 480 value 309.563138
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## iter 500 value 309.554007
## final value 309.554007
## stopped after 500 iterations
## # weights: 141
## initial value 1398016.686222
## iter 10 value 1565.105950
## iter 20 value 1099.277338
## iter 30 value 899.889456
## iter 40 value 778.738743
## iter 50 value 699.333858
## iter 60 value 622.568080
## iter 70 value 534.120331
## iter 80 value 475.671982
## iter 90 value 444.124394
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## iter 470 value 157.202095
## iter 480 value 155.260796
## iter 490 value 153.961776
## iter 500 value 152.949283
## final value 152.949283
## stopped after 500 iterations
## # weights: 15
## initial value 1427199.364592
## iter 10 value 3242.688199
## iter 20 value 2249.407741
## iter 30 value 1595.837381
## iter 40 value 1557.964498
## iter 50 value 1491.031424
## iter 60 value 1457.539530
## iter 70 value 1448.962899
## iter 80 value 1442.410842
## iter 90 value 1426.997669
## iter 100 value 1421.633111
## iter 110 value 1420.373954
## iter 120 value 1418.315760
## iter 130 value 1416.125748
## iter 140 value 1415.880539
## iter 150 value 1413.615922
## iter 160 value 1412.632274
## iter 170 value 1412.410624
## iter 180 value 1411.948765
## iter 190 value 1410.971972
## iter 200 value 1410.942898
## iter 210 value 1410.518671
## iter 220 value 1409.852664
## iter 230 value 1409.831473
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## iter 280 value 1409.043085
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## iter 300 value 1408.822682
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## iter 320 value 1408.776997
## iter 330 value 1408.658837
## iter 340 value 1408.571740
## iter 350 value 1408.546493
## iter 360 value 1408.465372
## iter 370 value 1408.393801
## final value 1408.382644
## converged
## # weights: 36
## initial value 1387443.119112
## iter 10 value 5029.065347
## iter 20 value 2912.972548
## iter 30 value 2237.542984
## iter 40 value 2006.347229
## iter 50 value 1909.375173
## iter 60 value 1790.946282
## iter 70 value 1712.613145
## iter 80 value 1681.725961
## iter 90 value 1599.311611
## iter 100 value 1516.571936
## iter 110 value 1422.507640
## iter 120 value 1346.716619
## iter 130 value 1291.638892
## iter 140 value 1271.103397
## iter 150 value 1258.578434
## iter 160 value 1253.209060
## iter 170 value 1248.402212
## iter 180 value 1245.130798
## iter 190 value 1243.481291
## iter 200 value 1241.810653
## iter 210 value 1239.466220
## iter 220 value 1238.353674
## iter 230 value 1237.834079
## iter 240 value 1237.643670
## iter 240 value 1237.643663
## final value 1237.643421
## converged
## # weights: 71
## initial value 1360784.265008
## iter 10 value 2446.221325
## iter 20 value 1437.744290
## iter 30 value 1185.414210
## iter 40 value 1088.668630
## iter 50 value 1052.982109
## iter 60 value 1005.474316
## iter 70 value 986.691240
## iter 80 value 959.294830
## iter 90 value 903.219676
## iter 100 value 859.172852
## iter 110 value 836.883717
## iter 120 value 809.661391
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## iter 140 value 790.158781
## iter 150 value 780.159581
## iter 160 value 775.703920
## iter 170 value 767.937968
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## iter 200 value 726.770310
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## iter 230 value 672.577403
## iter 240 value 666.041811
## iter 250 value 658.411027
## iter 260 value 655.067148
## iter 270 value 652.181160
## iter 280 value 650.666221
## iter 290 value 650.035753
## iter 300 value 649.809694
## iter 310 value 649.175119
## iter 320 value 648.216135
## iter 330 value 647.233034
## iter 340 value 645.887567
## iter 350 value 645.033747
## iter 360 value 644.218599
## iter 370 value 643.494702
## iter 380 value 641.894373
## iter 390 value 630.928016
## iter 400 value 618.027459
## iter 410 value 611.767345
## iter 420 value 605.466233
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## iter 450 value 600.047340
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## iter 470 value 597.130243
## iter 480 value 592.026027
## iter 490 value 584.257597
## iter 500 value 574.192437
## final value 574.192437
## stopped after 500 iterations
## # weights: 106
## initial value 1366264.493128
## iter 10 value 1797.543543
## iter 20 value 1106.069376
## iter 30 value 974.949719
## iter 40 value 881.972848
## iter 50 value 793.602851
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## iter 90 value 597.506944
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## iter 170 value 408.823868
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## iter 200 value 372.902567
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## iter 280 value 350.999064
## iter 290 value 345.225527
## iter 300 value 335.930593
## iter 310 value 327.823448
## iter 320 value 322.815772
## iter 330 value 320.863011
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## iter 400 value 315.371832
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## iter 470 value 312.441780
## iter 480 value 312.224011
## iter 490 value 311.993086
## iter 500 value 311.869517
## final value 311.869517
## stopped after 500 iterations
## # weights: 141
## initial value 1372533.323581
## iter 10 value 1480.367520
## iter 20 value 1126.726901
## iter 30 value 988.676507
## iter 40 value 840.096714
## iter 50 value 762.684788
## iter 60 value 664.892252
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## iter 80 value 594.420480
## iter 90 value 564.531276
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## iter 210 value 335.391371
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## iter 230 value 312.566423
## iter 240 value 285.064343
## iter 250 value 256.470562
## iter 260 value 244.150241
## iter 270 value 239.503331
## iter 280 value 236.672330
## iter 290 value 235.262732
## iter 300 value 234.355997
## iter 310 value 232.702594
## iter 320 value 230.556446
## iter 330 value 226.494332
## iter 340 value 222.311119
## iter 350 value 219.220469
## iter 360 value 217.238618
## iter 370 value 213.503818
## iter 380 value 205.421389
## iter 390 value 200.221458
## iter 400 value 197.021718
## iter 410 value 192.395241
## iter 420 value 184.411012
## iter 430 value 180.493921
## iter 440 value 178.251529
## iter 450 value 177.501438
## iter 460 value 177.168799
## iter 470 value 176.316968
## iter 480 value 174.794190
## iter 490 value 173.118031
## iter 500 value 171.821966
## final value 171.821966
## stopped after 500 iterations
## # weights: 15
## initial value 1400219.291998
## iter 10 value 16325.030064
## iter 20 value 15066.481578
## iter 30 value 12981.858867
## iter 40 value 4474.582093
## iter 50 value 2971.566720
## iter 60 value 2575.484846
## iter 70 value 2028.998508
## iter 80 value 1925.122545
## iter 90 value 1923.681091
## final value 1923.234613
## converged
## # weights: 36
## initial value 1431727.198108
## iter 10 value 8284.986412
## iter 20 value 5295.495445
## iter 30 value 4118.975282
## iter 40 value 2633.104348
## iter 50 value 1940.944050
## iter 60 value 1679.685953
## iter 70 value 1598.626483
## iter 80 value 1507.322957
## iter 90 value 1438.811629
## iter 100 value 1419.836307
## iter 110 value 1376.251878
## iter 120 value 1325.691046
## iter 130 value 1289.892434
## iter 140 value 1281.453328
## iter 150 value 1278.971264
## iter 160 value 1278.561145
## final value 1278.560941
## converged
## # weights: 71
## initial value 1379117.849918
## iter 10 value 2989.077526
## iter 20 value 1955.114958
## iter 30 value 1633.446156
## iter 40 value 1424.928873
## iter 50 value 1370.391419
## iter 60 value 1311.298776
## iter 70 value 1275.096579
## iter 80 value 1227.828832
## iter 90 value 1178.794754
## iter 100 value 1138.618479
## iter 110 value 1115.821019
## iter 120 value 1092.705933
## iter 130 value 1039.559949
## iter 140 value 1003.059109
## iter 150 value 991.834398
## iter 160 value 983.513802
## iter 170 value 975.648985
## iter 180 value 970.955511
## iter 190 value 967.587718
## iter 200 value 955.113470
## iter 210 value 938.152614
## iter 220 value 923.314055
## iter 230 value 919.603360
## iter 240 value 918.475428
## iter 250 value 918.446634
## iter 260 value 918.443057
## final value 918.442995
## converged
## # weights: 106
## initial value 1410758.266674
## iter 10 value 2535.453940
## iter 20 value 1505.396895
## iter 30 value 1210.490180
## iter 40 value 1105.520629
## iter 50 value 1011.228998
## iter 60 value 933.757222
## iter 70 value 903.586133
## iter 80 value 891.623376
## iter 90 value 878.391404
## iter 100 value 863.905128
## iter 110 value 852.119581
## iter 120 value 836.922741
## iter 130 value 822.923312
## iter 140 value 805.659807
## iter 150 value 793.258493
## iter 160 value 782.291004
## iter 170 value 775.937624
## iter 180 value 767.753979
## iter 190 value 762.703005
## iter 200 value 749.728469
## iter 210 value 744.772040
## iter 220 value 740.534786
## iter 230 value 735.307256
## iter 240 value 729.975623
## iter 250 value 726.056542
## iter 260 value 721.853876
## iter 270 value 717.210090
## iter 280 value 710.256634
## iter 290 value 706.554727
## iter 300 value 703.789616
## iter 310 value 702.364107
## iter 320 value 700.224333
## iter 330 value 699.050215
## iter 340 value 697.660589
## iter 350 value 694.039316
## iter 360 value 692.655964
## iter 370 value 692.407043
## iter 380 value 692.306624
## iter 390 value 692.279008
## iter 400 value 692.266105
## iter 410 value 692.260321
## final value 692.259869
## converged
## # weights: 141
## initial value 1391336.697576
## iter 10 value 1679.134965
## iter 20 value 1160.969209
## iter 30 value 1012.610725
## iter 40 value 906.566783
## iter 50 value 838.487991
## iter 60 value 797.421465
## iter 70 value 764.611789
## iter 80 value 715.557849
## iter 90 value 692.928984
## iter 100 value 680.303625
## iter 110 value 674.730589
## iter 120 value 665.497172
## iter 130 value 645.807107
## iter 140 value 626.233835
## iter 150 value 615.015905
## iter 160 value 608.383590
## iter 170 value 603.948516
## iter 180 value 599.559590
## iter 190 value 595.078801
## iter 200 value 587.977366
## iter 210 value 581.186089
## iter 220 value 572.420636
## iter 230 value 569.002586
## iter 240 value 567.395920
## iter 250 value 566.056267
## iter 260 value 565.253453
## iter 270 value 564.491671
## iter 280 value 563.705579
## iter 290 value 563.256366
## iter 300 value 562.719545
## iter 310 value 561.778606
## iter 320 value 561.312043
## iter 330 value 561.071028
## iter 340 value 560.867456
## iter 350 value 560.582783
## iter 360 value 560.216370
## iter 370 value 559.260623
## iter 380 value 557.929919
## iter 390 value 556.965646
## iter 400 value 556.441930
## iter 410 value 555.893961
## iter 420 value 554.017782
## iter 430 value 551.394580
## iter 440 value 548.713322
## iter 450 value 547.269798
## iter 460 value 546.723920
## iter 470 value 546.286213
## iter 480 value 546.238980
## iter 490 value 546.228318
## iter 500 value 546.227711
## final value 546.227711
## stopped after 500 iterations
## # weights: 15
## initial value 1404826.362613
## iter 10 value 32721.877973
## iter 20 value 6453.845876
## iter 30 value 5737.494193
## iter 40 value 4633.977981
## iter 50 value 3619.024104
## iter 60 value 2526.020037
## iter 70 value 1808.841350
## iter 80 value 1556.222365
## iter 90 value 1497.069115
## iter 100 value 1484.569417
## iter 110 value 1469.349787
## iter 120 value 1461.292695
## iter 130 value 1459.511442
## iter 140 value 1459.149414
## iter 150 value 1457.858074
## iter 160 value 1457.761467
## iter 170 value 1457.733183
## iter 180 value 1457.673060
## final value 1457.659457
## converged
## # weights: 36
## initial value 1444227.392543
## iter 10 value 5062.236456
## iter 20 value 4675.618592
## iter 30 value 3435.776310
## iter 40 value 2876.615324
## iter 50 value 1721.785709
## iter 60 value 1462.511986
## iter 70 value 1384.303676
## iter 80 value 1325.151246
## iter 90 value 1261.684157
## iter 100 value 1251.574189
## iter 110 value 1219.100979
## iter 120 value 1161.555827
## iter 130 value 1082.304591
## iter 140 value 1020.980104
## iter 150 value 980.983478
## iter 160 value 957.879852
## iter 170 value 950.836718
## iter 180 value 948.142159
## iter 190 value 940.976788
## iter 200 value 939.305717
## iter 210 value 927.920022
## iter 220 value 901.157306
## iter 230 value 895.205046
## iter 240 value 892.141878
## iter 250 value 891.946879
## iter 260 value 891.754949
## iter 270 value 891.748145
## iter 270 value 891.748140
## final value 891.748116
## converged
## # weights: 71
## initial value 1429826.480921
## iter 10 value 3908.022721
## iter 20 value 1904.190811
## iter 30 value 1511.543426
## iter 40 value 1215.663584
## iter 50 value 1015.831856
## iter 60 value 952.940472
## iter 70 value 925.690631
## iter 80 value 910.993328
## iter 90 value 887.954082
## iter 100 value 865.824288
## iter 110 value 846.571250
## iter 120 value 833.556379
## iter 130 value 825.822751
## iter 140 value 816.510487
## iter 150 value 806.228886
## iter 160 value 800.818405
## iter 170 value 792.894778
## iter 180 value 787.195638
## iter 190 value 784.054576
## iter 200 value 780.978011
## iter 210 value 778.992896
## iter 220 value 777.771203
## iter 230 value 777.018584
## iter 240 value 776.140884
## iter 250 value 776.005139
## iter 260 value 774.062682
## iter 270 value 771.044483
## iter 280 value 761.139693
## iter 290 value 753.227927
## iter 300 value 741.255093
## iter 310 value 734.128351
## iter 320 value 728.859687
## iter 330 value 727.273290
## iter 340 value 726.289155
## iter 350 value 726.033905
## iter 360 value 725.606873
## iter 370 value 725.099174
## iter 380 value 723.368673
## iter 390 value 722.471860
## iter 400 value 722.172141
## iter 410 value 721.683755
## iter 420 value 720.940501
## iter 430 value 719.776472
## iter 440 value 718.354904
## iter 450 value 715.993559
## iter 460 value 714.307419
## iter 470 value 711.366624
## iter 480 value 704.823333
## iter 490 value 701.187206
## iter 500 value 699.438202
## final value 699.438202
## stopped after 500 iterations
## # weights: 106
## initial value 1395679.369946
## iter 10 value 1497.985843
## iter 20 value 1108.336926
## iter 30 value 951.659517
## iter 40 value 851.905880
## iter 50 value 768.214187
## iter 60 value 691.017469
## iter 70 value 633.929690
## iter 80 value 608.907073
## iter 90 value 582.681975
## iter 100 value 556.747459
## iter 110 value 536.767086
## iter 120 value 524.995917
## iter 130 value 514.789354
## iter 140 value 504.574917
## iter 150 value 487.104922
## iter 160 value 476.344342
## iter 170 value 465.535386
## iter 180 value 455.164252
## iter 190 value 445.192897
## iter 200 value 441.116022
## iter 210 value 439.150846
## iter 220 value 437.836622
## iter 230 value 437.171045
## iter 240 value 434.934696
## iter 250 value 432.346010
## iter 260 value 429.801991
## iter 270 value 427.981366
## iter 280 value 419.640661
## iter 290 value 403.940257
## iter 300 value 395.876854
## iter 310 value 386.488723
## iter 320 value 381.275499
## iter 330 value 377.688796
## iter 340 value 371.857174
## iter 350 value 366.901524
## iter 360 value 360.397980
## iter 370 value 355.648910
## iter 380 value 354.022210
## iter 390 value 353.280329
## iter 400 value 353.023956
## iter 410 value 352.872002
## iter 420 value 352.743042
## iter 430 value 352.657504
## iter 440 value 352.649331
## iter 450 value 352.634584
## iter 460 value 352.597679
## iter 470 value 352.529372
## iter 480 value 352.464494
## iter 490 value 352.423608
## iter 500 value 352.390627
## final value 352.390627
## stopped after 500 iterations
## # weights: 141
## initial value 1402396.474510
## iter 10 value 1565.384025
## iter 20 value 1095.466559
## iter 30 value 911.356603
## iter 40 value 812.853393
## iter 50 value 732.232344
## iter 60 value 665.504817
## iter 70 value 601.970845
## iter 80 value 527.318422
## iter 90 value 471.846724
## iter 100 value 435.855865
## iter 110 value 402.716331
## iter 120 value 368.427592
## iter 130 value 339.663207
## iter 140 value 318.397535
## iter 150 value 308.567002
## iter 160 value 298.716965
## iter 170 value 289.773355
## iter 180 value 279.397730
## iter 190 value 270.094790
## iter 200 value 262.558848
## iter 210 value 256.933785
## iter 220 value 248.609932
## iter 230 value 240.671532
## iter 240 value 235.995528
## iter 250 value 231.408183
## iter 260 value 229.583538
## iter 270 value 224.923008
## iter 280 value 221.256600
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## iter 300 value 219.521360
## iter 310 value 218.156805
## iter 320 value 214.315702
## iter 330 value 210.610226
## iter 340 value 208.201640
## iter 350 value 206.429068
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## iter 380 value 203.192205
## iter 390 value 201.312009
## iter 400 value 196.900440
## iter 410 value 195.181317
## iter 420 value 193.423232
## iter 430 value 192.595965
## iter 440 value 191.757948
## iter 450 value 191.145245
## iter 460 value 190.919524
## iter 470 value 190.849527
## iter 480 value 190.817376
## iter 490 value 190.797801
## iter 500 value 190.786654
## final value 190.786654
## stopped after 500 iterations
## # weights: 15
## initial value 1451473.195072
## iter 10 value 9999.874955
## iter 20 value 6284.155426
## iter 30 value 3408.293275
## iter 40 value 2346.660738
## iter 50 value 1861.534597
## iter 60 value 1804.594349
## iter 70 value 1797.358378
## iter 80 value 1787.590490
## iter 90 value 1779.595845
## iter 100 value 1764.417013
## iter 110 value 1742.360651
## iter 120 value 1680.786733
## iter 130 value 1581.952025
## iter 140 value 1560.219304
## iter 150 value 1532.758315
## iter 160 value 1526.297509
## iter 170 value 1525.765720
## iter 180 value 1524.909073
## iter 190 value 1523.339970
## iter 200 value 1523.247658
## final value 1523.247106
## converged
## # weights: 36
## initial value 1426398.049107
## iter 10 value 48432.755975
## iter 20 value 6519.976709
## iter 30 value 5381.745208
## iter 40 value 3391.785638
## iter 50 value 2359.933558
## iter 60 value 1951.955342
## iter 70 value 1638.893474
## iter 80 value 1544.165259
## iter 90 value 1368.390150
## iter 100 value 1205.954665
## iter 110 value 1153.346121
## iter 120 value 1105.196885
## iter 130 value 1079.219723
## iter 140 value 1060.185847
## iter 150 value 1057.518592
## iter 160 value 1054.773903
## iter 170 value 1044.352646
## iter 180 value 1027.110759
## iter 190 value 1018.048185
## iter 200 value 1013.520825
## iter 210 value 1009.233565
## iter 220 value 1006.930774
## iter 230 value 1006.877489
## iter 240 value 1005.724700
## iter 250 value 1004.701416
## iter 260 value 1002.664993
## iter 270 value 1001.475816
## iter 280 value 1001.002681
## iter 290 value 994.731485
## iter 300 value 993.200207
## iter 310 value 992.949445
## iter 320 value 992.477898
## iter 330 value 991.727619
## iter 340 value 991.567608
## iter 350 value 991.543638
## iter 360 value 991.414729
## iter 370 value 991.313927
## iter 380 value 990.812654
## iter 390 value 990.350406
## iter 400 value 990.296293
## iter 410 value 990.221590
## iter 420 value 990.212721
## iter 430 value 990.195420
## iter 440 value 990.189898
## iter 450 value 990.165524
## iter 460 value 990.126153
## iter 470 value 990.123638
## final value 990.118601
## converged
## # weights: 71
## initial value 1405972.882724
## iter 10 value 1915.326261
## iter 20 value 1194.196347
## iter 30 value 1118.476386
## iter 40 value 985.901710
## iter 50 value 919.250758
## iter 60 value 893.044280
## iter 70 value 881.790577
## iter 80 value 860.052497
## iter 90 value 809.760319
## iter 100 value 774.911884
## iter 110 value 755.643078
## iter 120 value 732.841669
## iter 130 value 719.510505
## iter 140 value 701.017700
## iter 150 value 692.489865
## iter 160 value 688.667336
## iter 170 value 682.649356
## iter 180 value 676.086047
## iter 190 value 671.327534
## iter 200 value 660.854934
## iter 210 value 653.604903
## iter 220 value 639.407987
## iter 230 value 636.521749
## iter 240 value 635.594748
## iter 250 value 635.178167
## iter 260 value 634.368150
## iter 270 value 633.777238
## iter 280 value 633.701321
## iter 290 value 633.526592
## iter 300 value 633.501663
## iter 310 value 633.466699
## iter 320 value 633.402470
## iter 330 value 632.969770
## iter 340 value 627.240871
## iter 350 value 608.010389
## iter 360 value 599.090730
## iter 370 value 597.674555
## iter 380 value 597.485534
## iter 390 value 597.215531
## iter 400 value 596.999744
## iter 410 value 596.658741
## iter 420 value 596.607103
## iter 430 value 596.534104
## iter 440 value 596.440549
## iter 450 value 596.013803
## iter 460 value 595.929490
## iter 470 value 595.849942
## iter 480 value 595.785580
## iter 490 value 595.736454
## iter 500 value 595.700857
## final value 595.700857
## stopped after 500 iterations
## # weights: 106
## initial value 1418359.832409
## iter 10 value 1452.786704
## iter 20 value 1036.847032
## iter 30 value 920.106690
## iter 40 value 833.317077
## iter 50 value 766.130133
## iter 60 value 722.293706
## iter 70 value 668.257294
## iter 80 value 636.712826
## iter 90 value 618.548626
## iter 100 value 598.051080
## iter 110 value 578.634890
## iter 120 value 544.075382
## iter 130 value 528.564691
## iter 140 value 515.171536
## iter 150 value 505.497297
## iter 160 value 490.666183
## iter 170 value 476.795460
## iter 180 value 464.203983
## iter 190 value 450.534716
## iter 200 value 442.057696
## iter 210 value 428.269413
## iter 220 value 415.716016
## iter 230 value 411.805372
## iter 240 value 400.500323
## iter 250 value 389.409199
## iter 260 value 377.573731
## iter 270 value 367.017041
## iter 280 value 354.721519
## iter 290 value 347.659602
## iter 300 value 341.802013
## iter 310 value 331.436035
## iter 320 value 326.980036
## iter 330 value 323.097620
## iter 340 value 321.484679
## iter 350 value 320.750515
## iter 360 value 320.357687
## iter 370 value 320.036586
## iter 380 value 319.386035
## iter 390 value 318.978826
## iter 400 value 318.632261
## iter 410 value 318.566028
## iter 420 value 318.398991
## iter 430 value 318.330713
## iter 440 value 318.325070
## iter 450 value 318.318671
## iter 460 value 318.314279
## iter 470 value 318.307885
## iter 480 value 318.298592
## iter 490 value 318.274969
## iter 500 value 318.251595
## final value 318.251595
## stopped after 500 iterations
## # weights: 141
## initial value 1432813.655953
## iter 10 value 2102.635737
## iter 20 value 1185.653353
## iter 30 value 936.188605
## iter 40 value 805.788597
## iter 50 value 728.498664
## iter 60 value 689.484378
## iter 70 value 636.591584
## iter 80 value 587.900354
## iter 90 value 550.708603
## iter 100 value 503.608683
## iter 110 value 477.530651
## iter 120 value 451.850853
## iter 130 value 425.495600
## iter 140 value 401.364566
## iter 150 value 375.137777
## iter 160 value 339.916821
## iter 170 value 317.178937
## iter 180 value 300.947840
## iter 190 value 290.725855
## iter 200 value 269.207375
## iter 210 value 253.732018
## iter 220 value 242.727055
## iter 230 value 234.262680
## iter 240 value 227.394285
## iter 250 value 223.483319
## iter 260 value 217.864359
## iter 270 value 214.152285
## iter 280 value 208.303563
## iter 290 value 205.782290
## iter 300 value 204.416298
## iter 310 value 202.398359
## iter 320 value 201.313649
## iter 330 value 199.420724
## iter 340 value 196.697899
## iter 350 value 194.264986
## iter 360 value 192.218276
## iter 370 value 188.909482
## iter 380 value 182.890105
## iter 390 value 179.637395
## iter 400 value 177.989207
## iter 410 value 174.742177
## iter 420 value 171.907411
## iter 430 value 169.385245
## iter 440 value 166.739346
## iter 450 value 164.619503
## iter 460 value 163.896160
## iter 470 value 163.223401
## iter 480 value 162.397472
## iter 490 value 162.182061
## iter 500 value 161.988318
## final value 161.988318
## stopped after 500 iterations
## # weights: 15
## initial value 1390034.197438
## iter 10 value 13892.124029
## iter 20 value 6562.188313
## iter 30 value 3051.444410
## iter 40 value 2127.421799
## iter 50 value 1876.204600
## iter 60 value 1826.133665
## iter 70 value 1802.185099
## iter 80 value 1791.544467
## iter 90 value 1787.494721
## iter 100 value 1785.775576
## iter 110 value 1783.312243
## iter 120 value 1782.484121
## final value 1782.483480
## converged
## # weights: 36
## initial value 1366184.001165
## iter 10 value 6654.551523
## iter 20 value 3417.026801
## iter 30 value 3157.240097
## iter 40 value 3043.722350
## iter 50 value 2927.381630
## iter 60 value 2812.134864
## iter 70 value 2226.318858
## iter 80 value 1869.583723
## iter 90 value 1703.540293
## iter 100 value 1581.067435
## iter 110 value 1507.632134
## iter 120 value 1477.089366
## iter 130 value 1448.334885
## iter 140 value 1432.439887
## iter 150 value 1422.224080
## iter 160 value 1411.459191
## iter 170 value 1408.426088
## iter 180 value 1402.467856
## iter 190 value 1398.844598
## iter 200 value 1392.989529
## iter 210 value 1392.297878
## iter 220 value 1392.110154
## iter 230 value 1391.997603
## iter 240 value 1391.940292
## iter 250 value 1391.821173
## iter 260 value 1391.703381
## iter 270 value 1391.586220
## iter 280 value 1391.563831
## iter 290 value 1391.550603
## iter 300 value 1391.537559
## iter 310 value 1391.505456
## iter 320 value 1391.495127
## iter 330 value 1391.477459
## iter 340 value 1391.450899
## final value 1391.443955
## converged
## # weights: 71
## initial value 1391813.114842
## iter 10 value 2657.100294
## iter 20 value 1634.379497
## iter 30 value 1252.532240
## iter 40 value 1131.097605
## iter 50 value 1059.148725
## iter 60 value 1021.456656
## iter 70 value 979.072678
## iter 80 value 922.713984
## iter 90 value 877.089914
## iter 100 value 833.580205
## iter 110 value 807.856865
## iter 120 value 786.434591
## iter 130 value 766.468394
## iter 140 value 755.635622
## iter 150 value 750.615626
## iter 160 value 748.927547
## iter 170 value 743.641113
## iter 180 value 723.671557
## iter 190 value 676.136056
## iter 200 value 646.064332
## iter 210 value 630.868904
## iter 220 value 628.582884
## iter 230 value 627.673696
## iter 240 value 627.206151
## iter 250 value 627.093884
## iter 260 value 627.035724
## iter 270 value 626.930539
## iter 280 value 626.799097
## iter 290 value 626.733688
## iter 300 value 626.720008
## iter 310 value 626.701682
## iter 320 value 626.682585
## iter 330 value 626.655762
## iter 340 value 626.640426
## iter 350 value 626.611153
## iter 360 value 626.581425
## iter 370 value 626.492494
## iter 380 value 626.297004
## iter 390 value 626.197880
## iter 400 value 626.142191
## iter 410 value 626.118176
## iter 420 value 626.110193
## iter 430 value 626.108231
## iter 430 value 626.108231
## final value 626.108231
## converged
## # weights: 106
## initial value 1347737.515088
## iter 10 value 1456.152890
## iter 20 value 1051.910948
## iter 30 value 957.944274
## iter 40 value 869.638904
## iter 50 value 803.947749
## iter 60 value 778.208427
## iter 70 value 755.243332
## iter 80 value 733.766593
## iter 90 value 712.589748
## iter 100 value 687.868965
## iter 110 value 659.591066
## iter 120 value 637.406535
## iter 130 value 620.310398
## iter 140 value 607.039906
## iter 150 value 590.667537
## iter 160 value 567.129722
## iter 170 value 548.984706
## iter 180 value 531.821781
## iter 190 value 524.878809
## iter 200 value 512.113325
## iter 210 value 500.144779
## iter 220 value 494.540525
## iter 230 value 491.317886
## iter 240 value 484.234859
## iter 250 value 477.131535
## iter 260 value 472.703976
## iter 270 value 470.226546
## iter 280 value 463.632302
## iter 290 value 457.317511
## iter 300 value 447.362587
## iter 310 value 442.759559
## iter 320 value 438.021809
## iter 330 value 434.094670
## iter 340 value 432.777422
## iter 350 value 431.375867
## iter 360 value 430.276205
## iter 370 value 429.770074
## iter 380 value 428.890657
## iter 390 value 427.135000
## iter 400 value 424.457959
## iter 410 value 421.697228
## iter 420 value 420.369075
## iter 430 value 419.803702
## iter 440 value 419.708702
## iter 450 value 419.599151
## iter 460 value 419.418681
## iter 470 value 418.998346
## iter 480 value 416.298606
## iter 490 value 413.662347
## iter 500 value 411.517987
## final value 411.517987
## stopped after 500 iterations
## # weights: 141
## initial value 1420653.045091
## iter 10 value 1711.164326
## iter 20 value 1110.907664
## iter 30 value 974.478876
## iter 40 value 877.348897
## iter 50 value 782.091139
## iter 60 value 679.990586
## iter 70 value 626.639526
## iter 80 value 557.541007
## iter 90 value 528.641102
## iter 100 value 498.316283
## iter 110 value 467.256378
## iter 120 value 439.128405
## iter 130 value 417.336519
## iter 140 value 387.636253
## iter 150 value 350.199237
## iter 160 value 316.187138
## iter 170 value 289.024498
## iter 180 value 268.293152
## iter 190 value 254.327908
## iter 200 value 242.466847
## iter 210 value 233.942305
## iter 220 value 221.951709
## iter 230 value 216.097933
## iter 240 value 209.986860
## iter 250 value 203.126497
## iter 260 value 198.046217
## iter 270 value 193.460140
## iter 280 value 190.334481
## iter 290 value 188.691913
## iter 300 value 187.153414
## iter 310 value 183.927521
## iter 320 value 180.992975
## iter 330 value 178.408392
## iter 340 value 175.608049
## iter 350 value 171.951506
## iter 360 value 169.115878
## iter 370 value 167.319167
## iter 380 value 165.323391
## iter 390 value 164.055264
## iter 400 value 161.973875
## iter 410 value 160.371288
## iter 420 value 158.622948
## iter 430 value 156.363277
## iter 440 value 154.606331
## iter 450 value 151.958988
## iter 460 value 148.972841
## iter 470 value 146.953390
## iter 480 value 145.769754
## iter 490 value 144.810802
## iter 500 value 144.295475
## final value 144.295475
## stopped after 500 iterations
## # weights: 15
## initial value 1424069.190252
## iter 10 value 6635.879391
## iter 20 value 5903.955599
## iter 30 value 5621.027754
## iter 40 value 5329.960127
## iter 50 value 5307.171748
## iter 60 value 5148.468381
## iter 70 value 3040.862786
## iter 80 value 2000.619272
## iter 90 value 1867.171755
## iter 100 value 1826.648976
## iter 110 value 1805.111883
## iter 120 value 1795.580194
## iter 130 value 1795.533810
## final value 1795.533624
## converged
## # weights: 36
## initial value 1443980.895185
## iter 10 value 7280.403142
## iter 20 value 4085.072536
## iter 30 value 2975.278007
## iter 40 value 1847.289436
## iter 50 value 1428.688958
## iter 60 value 1278.330422
## iter 70 value 1210.312262
## iter 80 value 1187.209394
## iter 90 value 1172.772049
## iter 100 value 1134.732123
## iter 110 value 1097.817867
## iter 120 value 1078.813174
## iter 130 value 1067.060421
## iter 140 value 1062.209518
## iter 150 value 1045.276789
## iter 160 value 1025.356024
## iter 170 value 1008.443855
## iter 180 value 1008.041913
## iter 190 value 1001.292018
## iter 200 value 993.480759
## iter 210 value 990.678807
## iter 220 value 987.734482
## iter 230 value 984.658447
## iter 240 value 982.672619
## iter 250 value 982.484801
## iter 260 value 982.388583
## iter 270 value 981.554356
## iter 280 value 981.399040
## iter 290 value 981.335401
## iter 300 value 981.137161
## iter 310 value 980.797101
## iter 320 value 980.779059
## iter 330 value 980.778186
## iter 340 value 980.749443
## iter 350 value 980.742110
## iter 360 value 980.733796
## iter 370 value 980.671317
## iter 380 value 980.602457
## iter 390 value 980.582106
## iter 400 value 980.581640
## final value 980.581611
## converged
## # weights: 71
## initial value 1374791.289966
## iter 10 value 4182.123958
## iter 20 value 2436.759607
## iter 30 value 1745.431098
## iter 40 value 1391.255354
## iter 50 value 1108.066002
## iter 60 value 943.730878
## iter 70 value 886.324368
## iter 80 value 825.999663
## iter 90 value 779.728138
## iter 100 value 755.351732
## iter 110 value 741.211483
## iter 120 value 734.594663
## iter 130 value 732.136351
## iter 140 value 729.832074
## iter 150 value 728.256347
## iter 160 value 727.810340
## iter 170 value 727.651120
## iter 180 value 727.607532
## iter 190 value 727.531994
## iter 200 value 727.506774
## iter 210 value 727.464843
## iter 220 value 727.438555
## iter 230 value 727.348023
## iter 240 value 727.248679
## iter 250 value 727.182550
## iter 260 value 727.144778
## iter 270 value 727.122134
## iter 280 value 727.109875
## iter 290 value 727.097412
## iter 300 value 727.089414
## iter 310 value 727.084121
## final value 727.084095
## converged
## # weights: 106
## initial value 1386376.518562
## iter 10 value 1247.294295
## iter 20 value 1038.179311
## iter 30 value 909.896658
## iter 40 value 823.815879
## iter 50 value 769.363055
## iter 60 value 720.898727
## iter 70 value 678.169966
## iter 80 value 647.781180
## iter 90 value 617.934369
## iter 100 value 563.129779
## iter 110 value 490.547015
## iter 120 value 429.173335
## iter 130 value 408.650063
## iter 140 value 377.261748
## iter 150 value 358.575244
## iter 160 value 346.642453
## iter 170 value 338.871959
## iter 180 value 334.002855
## iter 190 value 329.980320
## iter 200 value 327.466104
## iter 210 value 325.287960
## iter 220 value 323.993941
## iter 230 value 323.070865
## iter 240 value 321.718002
## iter 250 value 320.341605
## iter 260 value 319.149367
## iter 270 value 317.164050
## iter 280 value 315.816459
## iter 290 value 313.156483
## iter 300 value 309.147917
## iter 310 value 304.287366
## iter 320 value 300.633211
## iter 330 value 295.338694
## iter 340 value 292.412166
## iter 350 value 289.735876
## iter 360 value 287.979883
## iter 370 value 284.168822
## iter 380 value 272.178097
## iter 390 value 266.121787
## iter 400 value 264.037361
## iter 410 value 263.215245
## iter 420 value 261.608534
## iter 430 value 260.698217
## iter 440 value 260.617884
## iter 450 value 260.481716
## iter 460 value 260.410018
## iter 470 value 260.349893
## iter 480 value 260.074588
## iter 490 value 260.032616
## iter 500 value 259.955186
## final value 259.955186
## stopped after 500 iterations
## # weights: 141
## initial value 1409018.705112
## iter 10 value 1844.575466
## iter 20 value 1111.140329
## iter 30 value 964.658936
## iter 40 value 884.317005
## iter 50 value 776.252817
## iter 60 value 692.961189
## iter 70 value 633.867363
## iter 80 value 571.794127
## iter 90 value 522.391308
## iter 100 value 499.615265
## iter 110 value 476.708638
## iter 120 value 454.965722
## iter 130 value 435.481181
## iter 140 value 414.278938
## iter 150 value 385.732347
## iter 160 value 367.876911
## iter 170 value 347.448267
## iter 180 value 329.063970
## iter 190 value 309.370327
## iter 200 value 290.847554
## iter 210 value 278.631944
## iter 220 value 265.429359
## iter 230 value 253.150354
## iter 240 value 246.538322
## iter 250 value 241.199468
## iter 260 value 236.388921
## iter 270 value 233.155801
## iter 280 value 230.945762
## iter 290 value 229.819392
## iter 300 value 229.011413
## iter 310 value 228.007311
## iter 320 value 227.032619
## iter 330 value 226.056214
## iter 340 value 224.324973
## iter 350 value 221.525407
## iter 360 value 219.055554
## iter 370 value 216.455249
## iter 380 value 213.844314
## iter 390 value 209.720402
## iter 400 value 202.645145
## iter 410 value 197.949226
## iter 420 value 194.761329
## iter 430 value 192.530156
## iter 440 value 191.356093
## iter 450 value 190.046195
## iter 460 value 189.086656
## iter 470 value 188.074922
## iter 480 value 187.271616
## iter 490 value 186.690422
## iter 500 value 186.043633
## final value 186.043633
## stopped after 500 iterations
## # weights: 15
## initial value 1402720.581926
## iter 10 value 10320.611162
## iter 20 value 9033.891051
## iter 30 value 7754.065428
## iter 40 value 6072.915906
## iter 50 value 3738.458154
## iter 60 value 2320.873572
## iter 70 value 1797.419317
## iter 80 value 1616.992327
## iter 90 value 1589.411758
## iter 100 value 1542.523117
## iter 110 value 1515.046126
## iter 120 value 1510.328549
## iter 130 value 1509.538536
## final value 1509.505079
## converged
## # weights: 36
## initial value 1386194.489221
## iter 10 value 101875.460853
## iter 20 value 32959.671045
## iter 30 value 9987.939720
## iter 40 value 5698.298534
## iter 50 value 4283.741617
## iter 60 value 2501.632114
## iter 70 value 1937.359968
## iter 80 value 1552.409305
## iter 90 value 1393.179486
## iter 100 value 1339.420470
## iter 110 value 1311.804863
## iter 120 value 1283.072744
## iter 130 value 1216.184077
## iter 140 value 1182.339946
## iter 150 value 1178.101608
## iter 160 value 1177.042523
## iter 170 value 1176.411048
## iter 180 value 1175.705566
## iter 190 value 1175.403006
## iter 200 value 1174.711678
## iter 210 value 1173.689247
## iter 220 value 1172.612147
## iter 230 value 1172.159412
## iter 240 value 1172.013974
## final value 1172.010770
## converged
## # weights: 71
## initial value 1396053.569499
## iter 10 value 3348.408305
## iter 20 value 2030.413248
## iter 30 value 1668.129944
## iter 40 value 1464.036382
## iter 50 value 1398.572290
## iter 60 value 1308.307907
## iter 70 value 1245.618153
## iter 80 value 1198.630055
## iter 90 value 1172.895225
## iter 100 value 1105.224645
## iter 110 value 1064.487764
## iter 120 value 1042.163154
## iter 130 value 1030.645278
## iter 140 value 1024.109557
## iter 150 value 1021.947389
## iter 160 value 1017.164075
## iter 170 value 1013.411143
## iter 180 value 1011.925871
## iter 190 value 1011.391476
## iter 200 value 1011.095843
## iter 210 value 1010.946906
## iter 220 value 1008.553339
## iter 230 value 1006.690289
## iter 240 value 1006.566334
## final value 1006.560429
## converged
## # weights: 106
## initial value 1391516.432562
## iter 10 value 1651.167238
## iter 20 value 1251.083304
## iter 30 value 1115.024091
## iter 40 value 1004.709583
## iter 50 value 955.982378
## iter 60 value 916.445844
## iter 70 value 890.251077
## iter 80 value 870.628227
## iter 90 value 853.357247
## iter 100 value 839.758639
## iter 110 value 829.431755
## iter 120 value 822.898385
## iter 130 value 815.269611
## iter 140 value 800.052318
## iter 150 value 787.450236
## iter 160 value 773.064706
## iter 170 value 759.697076
## iter 180 value 752.004200
## iter 190 value 740.734903
## iter 200 value 730.634272
## iter 210 value 726.799258
## iter 220 value 724.198612
## iter 230 value 720.027947
## iter 240 value 711.241997
## iter 250 value 704.217090
## iter 260 value 696.400582
## iter 270 value 693.056135
## iter 280 value 690.672336
## iter 290 value 687.886481
## iter 300 value 683.845858
## iter 310 value 681.197153
## iter 320 value 679.005369
## iter 330 value 677.799764
## iter 340 value 676.728619
## iter 350 value 676.424643
## iter 360 value 676.394985
## iter 370 value 676.394244
## final value 676.394219
## converged
## # weights: 141
## initial value 1431966.362448
## iter 10 value 1490.706632
## iter 20 value 1209.389376
## iter 30 value 1086.612162
## iter 40 value 1021.204629
## iter 50 value 959.489318
## iter 60 value 890.834848
## iter 70 value 855.744224
## iter 80 value 829.290153
## iter 90 value 793.608643
## iter 100 value 768.599793
## iter 110 value 744.324212
## iter 120 value 726.997448
## iter 130 value 707.120376
## iter 140 value 689.025439
## iter 150 value 681.849741
## iter 160 value 672.184899
## iter 170 value 663.316771
## iter 180 value 655.752892
## iter 190 value 649.283134
## iter 200 value 641.346472
## iter 210 value 634.067406
## iter 220 value 628.673020
## iter 230 value 620.702521
## iter 240 value 612.467706
## iter 250 value 609.368916
## iter 260 value 604.927903
## iter 270 value 599.893364
## iter 280 value 596.409329
## iter 290 value 595.198555
## iter 300 value 593.794668
## iter 310 value 591.448377
## iter 320 value 589.881803
## iter 330 value 589.251423
## iter 340 value 588.660829
## iter 350 value 588.011147
## iter 360 value 587.094786
## iter 370 value 585.945002
## iter 380 value 583.215844
## iter 390 value 581.510017
## iter 400 value 581.068589
## iter 410 value 580.852576
## iter 420 value 580.803359
## iter 430 value 580.769404
## iter 440 value 580.762703
## iter 450 value 580.760563
## final value 580.760385
## converged
## # weights: 15
## initial value 1397002.019183
## iter 10 value 20629.923574
## iter 20 value 11027.841073
## iter 30 value 5856.981940
## iter 40 value 5142.641439
## iter 50 value 4209.857075
## iter 60 value 2361.599064
## iter 70 value 2064.151928
## iter 80 value 1847.053856
## iter 90 value 1823.885867
## iter 100 value 1814.551563
## iter 110 value 1804.181113
## iter 120 value 1800.767789
## iter 130 value 1799.632021
## iter 140 value 1799.219463
## iter 150 value 1794.636456
## iter 160 value 1773.915327
## iter 170 value 1681.441716
## iter 180 value 1659.948644
## final value 1659.423588
## converged
## # weights: 36
## initial value 1414096.251600
## iter 10 value 2709.437795
## iter 20 value 1788.897752
## iter 30 value 1391.267360
## iter 40 value 1223.880107
## iter 50 value 1144.054983
## iter 60 value 1098.565361
## iter 70 value 1087.314627
## iter 80 value 1084.105204
## iter 90 value 1081.465356
## iter 100 value 1073.438628
## iter 110 value 1065.763494
## iter 120 value 1039.704350
## iter 130 value 986.244055
## iter 140 value 970.564976
## iter 150 value 965.563762
## iter 160 value 963.367023
## iter 170 value 958.152862
## iter 180 value 945.289910
## iter 190 value 940.633947
## iter 200 value 940.321929
## iter 210 value 940.255243
## iter 220 value 940.253476
## iter 220 value 940.253476
## final value 940.253476
## converged
## # weights: 71
## initial value 1394253.632215
## iter 10 value 3200.628477
## iter 20 value 1735.099501
## iter 30 value 1322.297661
## iter 40 value 1099.320053
## iter 50 value 999.444518
## iter 60 value 920.008910
## iter 70 value 896.405903
## iter 80 value 872.327153
## iter 90 value 845.528394
## iter 100 value 807.287629
## iter 110 value 745.245386
## iter 120 value 717.940599
## iter 130 value 698.521190
## iter 140 value 692.137863
## iter 150 value 690.002982
## iter 160 value 688.491148
## iter 170 value 687.464151
## iter 180 value 685.611881
## iter 190 value 680.417343
## iter 200 value 675.012656
## iter 210 value 671.782331
## iter 220 value 671.287493
## iter 230 value 669.802195
## iter 240 value 665.191673
## iter 250 value 659.674633
## iter 260 value 653.609435
## iter 270 value 648.654302
## iter 280 value 647.029240
## iter 290 value 645.609802
## iter 300 value 645.188807
## iter 310 value 645.168340
## iter 320 value 645.165064
## final value 645.164719
## converged
## # weights: 106
## initial value 1415252.908137
## iter 10 value 1736.174153
## iter 20 value 1149.296464
## iter 30 value 911.536294
## iter 40 value 810.067270
## iter 50 value 752.509179
## iter 60 value 679.659254
## iter 70 value 626.473432
## iter 80 value 585.425804
## iter 90 value 548.181105
## iter 100 value 523.018442
## iter 110 value 505.674216
## iter 120 value 498.789073
## iter 130 value 492.719132
## iter 140 value 486.714055
## iter 150 value 477.469363
## iter 160 value 469.654197
## iter 170 value 459.895522
## iter 180 value 451.245040
## iter 190 value 447.538415
## iter 200 value 443.426293
## iter 210 value 440.581463
## iter 220 value 439.294453
## iter 230 value 438.385818
## iter 240 value 435.053121
## iter 250 value 429.012767
## iter 260 value 421.840405
## iter 270 value 418.507067
## iter 280 value 413.529762
## iter 290 value 399.861418
## iter 300 value 394.881998
## iter 310 value 391.752172
## iter 320 value 385.919022
## iter 330 value 382.142778
## iter 340 value 377.223991
## iter 350 value 370.636767
## iter 360 value 363.643239
## iter 370 value 361.159591
## iter 380 value 359.541919
## iter 390 value 358.772838
## iter 400 value 358.511118
## iter 410 value 358.411810
## iter 420 value 358.240310
## iter 430 value 358.095445
## iter 440 value 358.062397
## iter 450 value 358.019919
## iter 460 value 357.945672
## iter 470 value 357.866273
## iter 480 value 357.796127
## iter 490 value 357.621143
## iter 500 value 357.390823
## final value 357.390823
## stopped after 500 iterations
## # weights: 141
## initial value 1312625.836615
## iter 10 value 1413.188581
## iter 20 value 1121.974937
## iter 30 value 954.382988
## iter 40 value 859.442334
## iter 50 value 753.920929
## iter 60 value 688.836022
## iter 70 value 647.293677
## iter 80 value 605.205853
## iter 90 value 538.787800
## iter 100 value 483.570881
## iter 110 value 449.156990
## iter 120 value 419.509644
## iter 130 value 394.513559
## iter 140 value 379.894016
## iter 150 value 368.220254
## iter 160 value 361.684027
## iter 170 value 355.353624
## iter 180 value 347.444538
## iter 190 value 342.048100
## iter 200 value 334.999553
## iter 210 value 329.043016
## iter 220 value 321.844182
## iter 230 value 315.803421
## iter 240 value 310.175060
## iter 250 value 307.852459
## iter 260 value 304.620041
## iter 270 value 302.315840
## iter 280 value 300.639048
## iter 290 value 299.409899
## iter 300 value 298.711622
## iter 310 value 297.499185
## iter 320 value 295.866229
## iter 330 value 293.362930
## iter 340 value 288.647338
## iter 350 value 284.678824
## iter 360 value 279.715284
## iter 370 value 275.806177
## iter 380 value 272.317030
## iter 390 value 267.148309
## iter 400 value 262.518896
## iter 410 value 259.120087
## iter 420 value 255.959338
## iter 430 value 253.864496
## iter 440 value 252.479472
## iter 450 value 251.100340
## iter 460 value 249.150001
## iter 470 value 243.644885
## iter 480 value 241.255169
## iter 490 value 238.884555
## iter 500 value 237.091930
## final value 237.091930
## stopped after 500 iterations
## # weights: 15
## initial value 1413533.660184
## iter 10 value 4721.894089
## iter 20 value 3416.384627
## iter 30 value 1927.536574
## iter 40 value 1800.717388
## iter 50 value 1666.988726
## iter 60 value 1641.844646
## iter 70 value 1575.835349
## iter 80 value 1507.321630
## iter 90 value 1444.003188
## iter 100 value 1274.923782
## iter 110 value 1259.461330
## iter 120 value 1253.175657
## iter 130 value 1236.620574
## iter 140 value 1230.732785
## iter 150 value 1228.920349
## iter 160 value 1225.804320
## iter 170 value 1223.734911
## iter 180 value 1223.569297
## iter 190 value 1222.930499
## iter 200 value 1222.541861
## iter 210 value 1222.487218
## iter 220 value 1222.162071
## iter 230 value 1221.655654
## iter 240 value 1221.612406
## iter 250 value 1221.582362
## iter 260 value 1221.519695
## final value 1221.516965
## converged
## # weights: 36
## initial value 1392306.769012
## iter 10 value 3495.866684
## iter 20 value 2586.700688
## iter 30 value 2393.753491
## iter 40 value 2131.211720
## iter 50 value 2057.587791
## iter 60 value 2017.415093
## iter 70 value 1853.602011
## iter 80 value 1683.490499
## iter 90 value 1505.011661
## iter 100 value 1338.334097
## iter 110 value 1200.576569
## iter 120 value 1179.235350
## iter 130 value 1167.690896
## iter 140 value 1162.949527
## iter 150 value 1157.261885
## iter 160 value 1156.575128
## iter 170 value 1156.552972
## iter 180 value 1156.329713
## iter 190 value 1156.279126
## iter 200 value 1154.874673
## iter 210 value 1153.741750
## iter 220 value 1153.678035
## iter 230 value 1151.277530
## iter 240 value 1150.348107
## final value 1150.244051
## converged
## # weights: 71
## initial value 1430457.741955
## iter 10 value 2111.775832
## iter 20 value 1196.036583
## iter 30 value 1054.648346
## iter 40 value 952.624033
## iter 50 value 903.238783
## iter 60 value 857.466852
## iter 70 value 825.161480
## iter 80 value 791.499398
## iter 90 value 771.815535
## iter 100 value 740.380278
## iter 110 value 724.700888
## iter 120 value 715.402902
## iter 130 value 703.822034
## iter 140 value 692.671990
## iter 150 value 687.258316
## iter 160 value 683.371189
## iter 170 value 675.911088
## iter 180 value 670.068800
## iter 190 value 656.565668
## iter 200 value 645.747245
## iter 210 value 636.675883
## iter 220 value 630.720232
## iter 230 value 617.075232
## iter 240 value 608.103878
## iter 250 value 603.319656
## iter 260 value 601.952011
## iter 270 value 597.352497
## iter 280 value 594.656861
## iter 290 value 592.652158
## iter 300 value 590.060822
## iter 310 value 588.307095
## iter 320 value 584.803095
## iter 330 value 580.003906
## iter 340 value 578.884971
## iter 350 value 578.644450
## iter 360 value 578.040543
## iter 370 value 577.074206
## iter 380 value 573.990947
## iter 390 value 572.418554
## iter 400 value 572.002550
## iter 410 value 571.070268
## iter 420 value 570.537695
## iter 430 value 570.431509
## iter 440 value 570.045800
## iter 450 value 568.596114
## iter 460 value 566.745335
## iter 470 value 566.000581
## iter 480 value 564.856702
## iter 490 value 564.784314
## iter 500 value 564.616962
## final value 564.616962
## stopped after 500 iterations
## # weights: 106
## initial value 1461657.112234
## iter 10 value 1739.933571
## iter 20 value 1129.764524
## iter 30 value 944.472279
## iter 40 value 841.536783
## iter 50 value 790.010241
## iter 60 value 739.058525
## iter 70 value 700.215976
## iter 80 value 675.755498
## iter 90 value 653.998602
## iter 100 value 627.266987
## iter 110 value 606.752424
## iter 120 value 582.285879
## iter 130 value 564.673206
## iter 140 value 546.879996
## iter 150 value 529.985040
## iter 160 value 513.233184
## iter 170 value 498.716100
## iter 180 value 474.959332
## iter 190 value 458.375545
## iter 200 value 446.665097
## iter 210 value 432.422076
## iter 220 value 426.593379
## iter 230 value 424.377783
## iter 240 value 413.280879
## iter 250 value 405.589091
## iter 260 value 392.814315
## iter 270 value 383.194085
## iter 280 value 374.911029
## iter 290 value 364.397982
## iter 300 value 353.497896
## iter 310 value 342.664572
## iter 320 value 335.903219
## iter 330 value 332.966207
## iter 340 value 332.199813
## iter 350 value 331.649081
## iter 360 value 331.210729
## iter 370 value 330.898721
## iter 380 value 329.693599
## iter 390 value 327.573093
## iter 400 value 326.216246
## iter 410 value 325.977657
## iter 420 value 325.741942
## iter 430 value 325.608225
## iter 440 value 325.591001
## iter 450 value 325.558885
## iter 460 value 325.506084
## iter 470 value 325.411805
## iter 480 value 325.303592
## iter 490 value 325.192868
## iter 500 value 324.892144
## final value 324.892144
## stopped after 500 iterations
## # weights: 141
## initial value 1426004.080425
## iter 10 value 1657.822788
## iter 20 value 1208.709292
## iter 30 value 1029.073809
## iter 40 value 900.484445
## iter 50 value 783.050196
## iter 60 value 702.087542
## iter 70 value 645.425211
## iter 80 value 602.557742
## iter 90 value 537.711814
## iter 100 value 504.170713
## iter 110 value 471.346255
## iter 120 value 450.171713
## iter 130 value 430.162608
## iter 140 value 406.953514
## iter 150 value 389.325455
## iter 160 value 374.075461
## iter 170 value 363.409511
## iter 180 value 354.457831
## iter 190 value 340.870779
## iter 200 value 329.537819
## iter 210 value 310.796943
## iter 220 value 298.272530
## iter 230 value 287.306282
## iter 240 value 276.529411
## iter 250 value 266.324739
## iter 260 value 261.078707
## iter 270 value 255.930320
## iter 280 value 251.011927
## iter 290 value 248.903234
## iter 300 value 247.621877
## iter 310 value 246.093950
## iter 320 value 243.733781
## iter 330 value 240.558099
## iter 340 value 235.914055
## iter 350 value 231.871406
## iter 360 value 227.510080
## iter 370 value 224.205130
## iter 380 value 220.245469
## iter 390 value 212.807360
## iter 400 value 208.670687
## iter 410 value 206.763314
## iter 420 value 205.485329
## iter 430 value 203.864056
## iter 440 value 202.487586
## iter 450 value 200.880771
## iter 460 value 200.316046
## iter 470 value 199.921837
## iter 480 value 199.252679
## iter 490 value 197.999137
## iter 500 value 197.005525
## final value 197.005525
## stopped after 500 iterations
## # weights: 15
## initial value 1408432.479508
## iter 10 value 15171.895631
## iter 20 value 6792.528075
## iter 30 value 3106.481551
## iter 40 value 2605.690059
## iter 50 value 2042.786602
## iter 60 value 1713.140333
## iter 70 value 1696.961086
## iter 80 value 1660.309124
## iter 90 value 1645.545975
## iter 100 value 1643.237576
## iter 110 value 1641.871108
## iter 120 value 1635.572779
## iter 130 value 1633.842065
## iter 140 value 1633.714580
## iter 150 value 1631.474656
## iter 160 value 1629.589204
## iter 170 value 1629.567523
## iter 180 value 1628.106566
## iter 190 value 1627.055879
## iter 200 value 1626.921655
## iter 210 value 1626.429491
## iter 220 value 1625.880181
## iter 230 value 1625.851738
## iter 240 value 1625.837352
## iter 250 value 1625.595330
## iter 260 value 1625.500214
## final value 1625.500173
## converged
## # weights: 36
## initial value 1424448.457589
## iter 10 value 4575.681223
## iter 20 value 2686.100236
## iter 30 value 2247.465147
## iter 40 value 1782.994432
## iter 50 value 1403.011423
## iter 60 value 1247.919580
## iter 70 value 1222.091882
## iter 80 value 1208.664119
## iter 90 value 1202.077338
## iter 100 value 1186.449513
## iter 110 value 1171.599896
## iter 120 value 1167.843354
## iter 130 value 1163.283684
## iter 140 value 1160.881349
## iter 150 value 1160.530210
## iter 160 value 1160.500446
## iter 170 value 1160.395427
## iter 180 value 1160.271370
## iter 190 value 1160.179868
## iter 200 value 1160.148109
## iter 210 value 1160.000179
## iter 220 value 1159.491818
## iter 230 value 1159.264864
## iter 240 value 1158.627279
## iter 250 value 1155.722644
## iter 260 value 1154.730701
## iter 270 value 1154.647463
## iter 280 value 1154.535382
## iter 290 value 1154.498254
## iter 300 value 1154.488706
## iter 310 value 1154.488246
## final value 1154.488196
## converged
## # weights: 71
## initial value 1417557.681487
## iter 10 value 2080.368532
## iter 20 value 1381.057850
## iter 30 value 1142.244345
## iter 40 value 934.010545
## iter 50 value 850.350536
## iter 60 value 797.620170
## iter 70 value 767.714937
## iter 80 value 738.635854
## iter 90 value 720.785523
## iter 100 value 679.792567
## iter 110 value 656.579060
## iter 120 value 641.181615
## iter 130 value 630.408704
## iter 140 value 619.613237
## iter 150 value 611.032133
## iter 160 value 609.037849
## iter 170 value 605.680429
## iter 180 value 600.468600
## iter 190 value 592.089937
## iter 200 value 578.679366
## iter 210 value 570.460557
## iter 220 value 561.256566
## iter 230 value 550.447676
## iter 240 value 545.834065
## iter 250 value 542.819047
## iter 260 value 541.366544
## iter 270 value 540.492214
## iter 280 value 540.215353
## iter 290 value 539.994488
## iter 300 value 539.949136
## iter 310 value 539.901212
## iter 320 value 539.839825
## iter 330 value 539.721864
## iter 340 value 539.328052
## iter 350 value 539.138437
## iter 360 value 538.758330
## iter 370 value 538.473643
## iter 380 value 538.199832
## iter 390 value 537.976579
## iter 400 value 537.780806
## iter 410 value 537.645212
## iter 420 value 537.530034
## iter 430 value 537.438562
## iter 440 value 537.436739
## iter 450 value 537.406417
## iter 460 value 537.336946
## iter 470 value 537.242874
## iter 480 value 537.101962
## iter 490 value 536.937019
## iter 500 value 536.788472
## final value 536.788472
## stopped after 500 iterations
## # weights: 106
## initial value 1460565.877414
## iter 10 value 2604.696195
## iter 20 value 1231.521063
## iter 30 value 1043.394233
## iter 40 value 948.020052
## iter 50 value 833.118880
## iter 60 value 737.124196
## iter 70 value 691.535713
## iter 80 value 661.927489
## iter 90 value 621.981945
## iter 100 value 592.558131
## iter 110 value 560.419370
## iter 120 value 535.872650
## iter 130 value 512.997449
## iter 140 value 504.306197
## iter 150 value 496.839702
## iter 160 value 487.764107
## iter 170 value 481.545347
## iter 180 value 476.667601
## iter 190 value 473.778281
## iter 200 value 471.991909
## iter 210 value 468.848764
## iter 220 value 467.471528
## iter 230 value 466.745202
## iter 240 value 464.394145
## iter 250 value 461.843272
## iter 260 value 454.347278
## iter 270 value 450.364408
## iter 280 value 447.799654
## iter 290 value 445.246241
## iter 300 value 441.806379
## iter 310 value 440.266362
## iter 320 value 437.451780
## iter 330 value 431.787413
## iter 340 value 422.709456
## iter 350 value 414.622455
## iter 360 value 408.971817
## iter 370 value 404.770230
## iter 380 value 402.919852
## iter 390 value 401.756895
## iter 400 value 400.851514
## iter 410 value 399.982132
## iter 420 value 399.406058
## iter 430 value 398.852108
## iter 440 value 398.713542
## iter 450 value 398.434726
## iter 460 value 398.097170
## iter 470 value 397.907875
## iter 480 value 397.747425
## iter 490 value 397.677596
## iter 500 value 397.622073
## final value 397.622073
## stopped after 500 iterations
## # weights: 141
## initial value 1390515.760772
## iter 10 value 1573.988546
## iter 20 value 1109.118264
## iter 30 value 964.094875
## iter 40 value 858.884226
## iter 50 value 771.939768
## iter 60 value 719.022903
## iter 70 value 645.597443
## iter 80 value 576.288972
## iter 90 value 540.378345
## iter 100 value 499.671779
## iter 110 value 474.404980
## iter 120 value 451.229096
## iter 130 value 430.808501
## iter 140 value 409.024914
## iter 150 value 393.055717
## iter 160 value 377.187053
## iter 170 value 361.390700
## iter 180 value 353.273033
## iter 190 value 346.860592
## iter 200 value 338.202899
## iter 210 value 328.800874
## iter 220 value 322.312555
## iter 230 value 315.232826
## iter 240 value 306.416530
## iter 250 value 298.244246
## iter 260 value 291.495383
## iter 270 value 282.160253
## iter 280 value 263.279304
## iter 290 value 257.664835
## iter 300 value 254.656368
## iter 310 value 248.739981
## iter 320 value 244.706123
## iter 330 value 239.372479
## iter 340 value 235.700617
## iter 350 value 230.732967
## iter 360 value 223.326551
## iter 370 value 216.920794
## iter 380 value 212.856378
## iter 390 value 206.050621
## iter 400 value 197.978055
## iter 410 value 194.327983
## iter 420 value 192.579616
## iter 430 value 190.283316
## iter 440 value 188.152264
## iter 450 value 186.594779
## iter 460 value 184.864735
## iter 470 value 183.956478
## iter 480 value 183.442826
## iter 490 value 182.820518
## iter 500 value 182.093269
## final value 182.093269
## stopped after 500 iterations
## # weights: 15
## initial value 1527080.175508
## iter 10 value 102805.349590
## iter 20 value 18306.866180
## iter 30 value 11220.529339
## iter 40 value 9758.335008
## iter 50 value 5584.599486
## iter 60 value 2593.036654
## iter 70 value 2314.360343
## iter 80 value 2074.127736
## iter 90 value 1707.529792
## iter 100 value 1632.459034
## iter 110 value 1620.942217
## iter 120 value 1601.733505
## iter 130 value 1600.435375
## iter 140 value 1600.420419
## final value 1600.408066
## converged
##################################
# Reporting the cross-validation results
# for the NN model
##################################
NN_Tune## Neural Network
##
## 294 samples
## 5 predictor
##
## Pre-processing: centered (4), scaled (4), ignore (1)
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 266, 264, 265, 264, 265, 266, ...
## Resampling results across tuning parameters:
##
## size decay RMSE Rsquared MAE
## 2 0e+00 3.459517 0.8369414 2.761631
## 2 1e-05 2.493429 0.9074457 1.926148
## 2 1e-04 2.477974 0.9067875 1.874481
## 2 1e-03 2.426691 0.9115333 1.809876
## 2 1e-01 2.353487 0.9162047 1.783663
## 5 0e+00 3.983000 0.7736723 2.182406
## 5 1e-05 2.545260 0.9014702 1.936831
## 5 1e-04 2.917347 0.8648001 1.990064
## 5 1e-03 2.579639 0.8959853 1.848074
## 5 1e-01 2.560729 0.9044140 1.805113
## 10 0e+00 2.941692 0.8734155 2.195624
## 10 1e-05 5.013236 0.7454131 2.569115
## 10 1e-04 4.104210 0.7609624 2.385276
## 10 1e-03 3.199424 0.8511775 2.303877
## 10 1e-01 2.831829 0.8769466 2.114228
## 15 0e+00 4.024030 0.7486857 2.691372
## 15 1e-05 4.260785 0.7695307 2.715268
## 15 1e-04 3.107588 0.8600351 2.333819
## 15 1e-03 3.461486 0.8201023 2.585248
## 15 1e-01 2.914087 0.8805032 2.135890
## 20 0e+00 4.050885 0.7894326 2.908419
## 20 1e-05 4.135612 0.7632034 2.943303
## 20 1e-04 3.706096 0.8182675 2.825326
## 20 1e-03 4.300572 0.7507562 3.033962
## 20 1e-01 3.155988 0.8634645 2.332871
##
## RMSE was used to select the optimal model using the smallest value.
## The final values used for the model were size = 2 and decay = 0.1.
NN_Tune$finalModel## a 5-2-1 network with 15 weights
## inputs: GENDERFemale INFMOR PERCAP CLTECH NCOMOR
## output(s): .outcome
## options were - linear output units decay=0.1
(NN_Tune_RMSE <- NN_Tune$results[NN_Tune$results$size==NN_Tune$bestTune$size &
NN_Tune$results$decay==NN_Tune$bestTune$decay,
c("RMSE")])## [1] 2.353487
(NN_Tune_Rsquared <- NN_Tune$results[NN_Tune$results$size==NN_Tune$bestTune$size &
NN_Tune$results$decay==NN_Tune$bestTune$decay,
c("Rsquared")])## [1] 0.9162047
(NN_Tune_MAE <- NN_Tune$results[NN_Tune$results$size==NN_Tune$bestTune$size &
NN_Tune$results$decay==NN_Tune$bestTune$decay,
c("MAE")])## [1] 1.783663
##################################
# Identifying and plotting the
# best model predictors
# for the NN model
##################################
NN_VarImp <- varImp(NN_Tune, scale = TRUE)
plot(NN_VarImp,
scales=list(y=list(cex = .95)),
main="Ranked Variable Importance : NN",
xlab="Scaled Variable Importance Metrics",
ylab="Predictors",
cex=2,
origin=0,
alpha=0.45)##################################
# Defining the model hyperparameter values
# for the PLS model
##################################
PLS_Grid = expand.grid(ncomp = 1:5)
##################################
# Running the PLS model
# by setting the caret method to 'pls'
##################################
set.seed(12345678)
PLS_Tune <- train(x = MD.Model.Predictors,
y = MD$LIFEXP,
method = "pls",
tuneGrid = PLS_Grid,
trControl = KFold_Control)
##################################
# Reporting the cross-validation results
# for the PLS model
##################################
PLS_Tune## Partial Least Squares
##
## 294 samples
## 5 predictor
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 266, 264, 265, 264, 265, 266, ...
## Resampling results across tuning parameters:
##
## ncomp RMSE Rsquared MAE
## 1 5.107641 0.5809693 4.152802
## 2 3.229570 0.8398823 2.428972
## 3 2.819900 0.8781609 2.095819
## 4 2.769699 0.8837208 2.114378
## 5 2.768609 0.8838715 2.110610
##
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was ncomp = 5.
PLS_Tune$finalModel## Partial least squares regression , fitted with the orthogonal scores algorithm.
## Call:
## plsr(formula = .outcome ~ ., ncomp = ncomp, data = dat, method = "oscorespls")
(PLS_Tune_RMSE <- PLS_Tune$results[PLS_Tune$results$ncomp==PLS_Tune$bestTune$ncomp,
c("RMSE")])## [1] 2.768609
(PLS_Tune_Rsquared <- PLS_Tune$results[PLS_Tune$results$ncomp==PLS_Tune$bestTune$ncomp,
c("Rsquared")])## [1] 0.8838715
(PLS_Tune_MAE <- PLS_Tune$results[PLS_Tune$results$ncomp==PLS_Tune$bestTune$ncomp,
c("MAE")])## [1] 2.11061
##################################
# Identifying and plotting the
# best model predictors
# for the PLS model
##################################
PLS_VarImp <- varImp(PLS_Tune, scale = TRUE)
plot(PLS_VarImp,
scales=list(y=list(cex = .95)),
main="Ranked Variable Importance : PLS",
xlab="Scaled Variable Importance Metrics",
ylab="Predictors",
cex=2,
origin=0,
alpha=0.45)##################################
# Defining the model hyperparameter values
# for the CUBIST model
##################################
CUBIST_Grid = expand.grid(committees = c(10, 20, 30, 40, 50),
neighbors = c(0, 3, 6, 9))
##################################
# Running the CUBIST model
# by setting the caret method to 'cubist'
##################################
set.seed(12345678)
CUBIST_Tune <- train(x = MD.Model.Predictors,
y = MD$LIFEXP,
method = "cubist",
tuneGrid = CUBIST_Grid,
trControl = KFold_Control)
##################################
# Reporting the cross-validation results
# for the CUBIST model
##################################
CUBIST_Tune## Cubist
##
## 294 samples
## 5 predictor
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 266, 264, 265, 264, 265, 266, ...
## Resampling results across tuning parameters:
##
## committees neighbors RMSE Rsquared MAE
## 10 0 2.349910 0.9176282 1.743119
## 10 3 2.463103 0.9118159 1.826258
## 10 6 2.414374 0.9147585 1.779205
## 10 9 2.371853 0.9172327 1.751077
## 20 0 2.325278 0.9194823 1.729501
## 20 3 2.452245 0.9128512 1.817801
## 20 6 2.403467 0.9158193 1.775160
## 20 9 2.365302 0.9179693 1.750785
## 30 0 2.288992 0.9216589 1.703604
## 30 3 2.441377 0.9135986 1.809325
## 30 6 2.385727 0.9169876 1.763454
## 30 9 2.346537 0.9191819 1.738438
## 40 0 2.288357 0.9211136 1.706122
## 40 3 2.436769 0.9136943 1.811555
## 40 6 2.383490 0.9168822 1.766773
## 40 9 2.345004 0.9190023 1.743124
## 50 0 2.283377 0.9214369 1.704048
## 50 3 2.434991 0.9139000 1.815273
## 50 6 2.382253 0.9170399 1.769362
## 50 9 2.343006 0.9192188 1.745291
##
## RMSE was used to select the optimal model using the smallest value.
## The final values used for the model were committees = 50 and neighbors = 0.
CUBIST_Tune$finalModel##
## Call:
## cubist.default(x = x, y = y, committees = param$committees)
##
## Number of samples: 294
## Number of predictors: 5
##
## Number of committees: 50
## Number of rules per committee: 6, 2, 4, 4, 5, 2, 4, 3, 4, 2, 4, 2, 3, 2, 4, 2, 3, 2, 4, 2 ...
(CUBIST_Tune_RMSE <- CUBIST_Tune$results[CUBIST_Tune$results$committees==CUBIST_Tune$bestTune$committees &
CUBIST_Tune$results$neighbors==CUBIST_Tune$bestTune$neighbors,
c("RMSE")])## [1] 2.283377
(CUBIST_Tune_Rsquared <- CUBIST_Tune$results[CUBIST_Tune$results$committees==CUBIST_Tune$bestTune$committees &
CUBIST_Tune$results$neighbors==CUBIST_Tune$bestTune$neighbors,
c("Rsquared")])## [1] 0.9214369
(CUBIST_Tune_MAE <- CUBIST_Tune$results[CUBIST_Tune$results$committees==CUBIST_Tune$bestTune$committees &
CUBIST_Tune$results$neighbors==CUBIST_Tune$bestTune$neighbors,
c("MAE")])## [1] 1.704048
##################################
# Identifying and plotting the
# best model predictors
# for the CUBIST model
##################################
CUBIST_VarImp <- varImp(CUBIST_Tune, scale = TRUE)
plot(CUBIST_VarImp,
scales=list(y=list(cex = .95)),
main="Ranked Variable Importance : CUBIST",
xlab="Scaled Variable Importance Metrics",
ylab="Predictors",
cex=2,
origin=0,
alpha=0.45)##################################
# Evaluating the models
# on the model test data
##################################
##################################
# Formulating the DALEX object
# for the Best GBM model
##################################
GBM_DALEX <- DALEX::explain(GBM_Tune,
data = MT.Model.Predictors,
y = MT$LIFEXP,
verbose = FALSE,
label = "GBM")
(GBM_DALEX_Performance <- model_performance(GBM_DALEX))## Measures for: regression
## mse : 3.493083
## rmse : 1.868979
## r2 : 0.9362058
## mad : 0.9972678
##
## Residuals:
## 0% 10% 20% 30% 40% 50%
## -4.20752484 -2.15021430 -1.48672408 -0.87866008 -0.39263579 -0.01705431
## 60% 70% 80% 90% 100%
## 0.35387527 0.67211084 1.30280583 2.02235201 6.02771793
(GBM_DALEX_Diagnostics <- model_diagnostics(GBM_DALEX))## GENDER INFMOR PERCAP CLTECH
## Male :43 Min. :0.5306 Min. :-1.4775 Min. : 0.20
## Female:29 1st Qu.:1.5623 1st Qu.: 0.8076 1st Qu.: 31.15
## Median :2.6602 Median : 1.5798 Median : 83.50
## Mean :2.5206 Mean : 1.7061 Mean : 67.12
## 3rd Qu.:3.4491 3rd Qu.: 2.8175 3rd Qu.:100.00
## Max. :4.4864 Max. : 4.2333 Max. :100.00
## NCOMOR y y_hat residuals
## Min. :2.511 Min. :53.79 Min. :56.81 Min. :-4.20753
## 1st Qu.:3.887 1st Qu.:67.49 1st Qu.:67.37 1st Qu.:-1.04218
## Median :4.685 Median :73.35 Median :72.36 Median :-0.01705
## Mean :4.736 Mean :72.42 Mean :72.48 Mean :-0.06016
## 3rd Qu.:5.631 3rd Qu.:78.39 3rd Qu.:78.86 3rd Qu.: 0.87535
## Max. :7.579 Max. :86.20 Max. :84.55 Max. : 6.02772
## abs_residuals label ids
## Min. :0.06685 Length:72 Min. : 1.00
## 1st Qu.:0.46126 Class :character 1st Qu.:18.75
## Median :0.99727 Mode :character Median :36.50
## Mean :1.40941 Mean :36.50
## 3rd Qu.:1.99579 3rd Qu.:54.25
## Max. :6.02772 Max. :72.00
plot(GBM_DALEX_Diagnostics,
variable = "y",
yvariable = "y_hat") +
geom_point(size=3) +
scale_x_continuous("Observed LIFEXP") +
scale_y_continuous("Predicted LIFEXP") +
geom_abline(slope = 1) +
ggtitle("GBM: Observed and Predicted LIFEXP")##################################
# Formulating the DALEX object
# for the Best RF model
##################################
RF_DALEX <- DALEX::explain(RF_Tune,
data = MT.Model.Predictors,
y = MT$LIFEXP,
verbose = FALSE,
label = "RF")
(RF_DALEX_Performance <- model_performance(RF_DALEX))## Measures for: regression
## mse : 4.504021
## rmse : 2.122268
## r2 : 0.917743
## mad : 1.284074
##
## Residuals:
## 0% 10% 20% 30% 40% 50%
## -5.22877943 -2.17233443 -1.33595313 -0.66251307 -0.31119611 0.01921082
## 60% 70% 80% 90% 100%
## 0.26151554 0.80194556 1.84018856 2.72166332 6.62793907
(RF_DALEX_Diagnostics <- model_diagnostics(RF_DALEX))## GENDER INFMOR PERCAP CLTECH
## Male :43 Min. :0.5306 Min. :-1.4775 Min. : 0.20
## Female:29 1st Qu.:1.5623 1st Qu.: 0.8076 1st Qu.: 31.15
## Median :2.6602 Median : 1.5798 Median : 83.50
## Mean :2.5206 Mean : 1.7061 Mean : 67.12
## 3rd Qu.:3.4491 3rd Qu.: 2.8175 3rd Qu.:100.00
## Max. :4.4864 Max. : 4.2333 Max. :100.00
## NCOMOR y y_hat residuals
## Min. :2.511 Min. :53.79 Min. :54.37 Min. :-5.22878
## 1st Qu.:3.887 1st Qu.:67.49 1st Qu.:68.13 1st Qu.:-1.20707
## Median :4.685 Median :73.35 Median :72.39 Median : 0.01921
## Mean :4.736 Mean :72.42 Mean :72.31 Mean : 0.11035
## 3rd Qu.:5.631 3rd Qu.:78.39 3rd Qu.:78.82 3rd Qu.: 1.46992
## Max. :7.579 Max. :86.20 Max. :84.60 Max. : 6.62794
## abs_residuals label ids
## Min. :0.03082 Length:72 Min. : 1.00
## 1st Qu.:0.46750 Class :character 1st Qu.:18.75
## Median :1.28407 Mode :character Median :36.50
## Mean :1.56501 Mean :36.50
## 3rd Qu.:2.23392 3rd Qu.:54.25
## Max. :6.62794 Max. :72.00
plot(RF_DALEX_Diagnostics,
variable = "y",
yvariable = "y_hat") +
geom_point(size=3) +
scale_x_continuous("Observed LIFEXP") +
scale_y_continuous("Predicted LIFEXP") +
geom_abline(slope = 1) +
ggtitle("RF: Observed and Predicted LIFEXP")##################################
# Formulating the DALEX object
# for the Best NN model
##################################
NN_DALEX <- DALEX::explain(NN_Tune,
data = MT.Model.Predictors,
y = MT$LIFEXP,
verbose = FALSE,
label = "NN")
(NN_DALEX_Performance <- model_performance(NN_DALEX))## Measures for: regression
## mse : 3.981273
## rmse : 1.995313
## r2 : 0.92729
## mad : 1.331898
##
## Residuals:
## 0% 10% 20% 30% 40% 50% 60%
## -5.0566398 -1.9253734 -1.4168918 -1.1691032 -0.3808666 0.2508203 0.3866501
## 70% 80% 90% 100%
## 1.0424427 1.7914332 2.9384548 4.8353156
(NN_DALEX_Diagnostics <- model_diagnostics(NN_DALEX))## GENDER INFMOR PERCAP CLTECH
## Male :43 Min. :0.5306 Min. :-1.4775 Min. : 0.20
## Female:29 1st Qu.:1.5623 1st Qu.: 0.8076 1st Qu.: 31.15
## Median :2.6602 Median : 1.5798 Median : 83.50
## Mean :2.5206 Mean : 1.7061 Mean : 67.12
## 3rd Qu.:3.4491 3rd Qu.: 2.8175 3rd Qu.:100.00
## Max. :4.4864 Max. : 4.2333 Max. :100.00
## NCOMOR y y_hat residuals
## Min. :2.511 Min. :53.79 Min. :53.77 Min. :-5.0566
## 1st Qu.:3.887 1st Qu.:67.49 1st Qu.:67.91 1st Qu.:-1.3453
## Median :4.685 Median :73.35 Median :72.39 Median : 0.2508
## Mean :4.736 Mean :72.42 Mean :72.30 Mean : 0.1240
## 3rd Qu.:5.631 3rd Qu.:78.39 3rd Qu.:78.90 3rd Qu.: 1.2255
## Max. :7.579 Max. :86.20 Max. :84.31 Max. : 4.8353
## abs_residuals label ids
## Min. :0.08507 Length:72 Min. : 1.00
## 1st Qu.:0.68395 Class :character 1st Qu.:18.75
## Median :1.33190 Mode :character Median :36.50
## Mean :1.58876 Mean :36.50
## 3rd Qu.:2.14093 3rd Qu.:54.25
## Max. :5.05664 Max. :72.00
plot(NN_DALEX_Diagnostics,
variable = "y",
yvariable = "y_hat") +
geom_point(size=3) +
scale_x_continuous("Observed LIFEXP") +
scale_y_continuous("Predicted LIFEXP") +
geom_abline(slope = 1) +
ggtitle("NN: Observed and Predicted LIFEXP")##################################
# Formulating the DALEX object
# for the Best PLS model
##################################
PLS_DALEX <- DALEX::explain(PLS_Tune,
data = MT.Model.Predictors,
y = MT$LIFEXP,
verbose = FALSE,
label = "PLS")
(PLS_DALEX_Performance <- model_performance(PLS_DALEX))## Measures for: regression
## mse : 8.772658
## rmse : 2.961867
## r2 : 0.8397849
## mad : 1.687233
##
## Residuals:
## 0% 10% 20% 30% 40% 50% 60%
## -8.2953487 -3.3619601 -2.3734314 -1.1325844 -0.4814388 0.1923809 0.7761085
## 70% 80% 90% 100%
## 1.3418691 2.3002494 4.1779864 6.2664318
(PLS_DALEX_Diagnostics <- model_diagnostics(PLS_DALEX))## GENDER INFMOR PERCAP CLTECH
## Male :43 Min. :0.5306 Min. :-1.4775 Min. : 0.20
## Female:29 1st Qu.:1.5623 1st Qu.: 0.8076 1st Qu.: 31.15
## Median :2.6602 Median : 1.5798 Median : 83.50
## Mean :2.5206 Mean : 1.7061 Mean : 67.12
## 3rd Qu.:3.4491 3rd Qu.: 2.8175 3rd Qu.:100.00
## Max. :4.4864 Max. : 4.2333 Max. :100.00
## NCOMOR y y_hat residuals
## Min. :2.511 Min. :53.79 Min. :58.14 Min. :-8.29535
## 1st Qu.:3.887 1st Qu.:67.49 1st Qu.:67.39 1st Qu.:-1.73571
## Median :4.685 Median :73.35 Median :72.32 Median : 0.19238
## Mean :4.736 Mean :72.42 Mean :72.34 Mean : 0.07797
## 3rd Qu.:5.631 3rd Qu.:78.39 3rd Qu.:78.60 3rd Qu.: 1.59971
## Max. :7.579 Max. :86.20 Max. :86.20 Max. : 6.26643
## abs_residuals label ids
## Min. :0.01028 Length:72 Min. : 1.00
## 1st Qu.:0.77705 Class :character 1st Qu.:18.75
## Median :1.68723 Mode :character Median :36.50
## Mean :2.27256 Mean :36.50
## 3rd Qu.:3.40953 3rd Qu.:54.25
## Max. :8.29535 Max. :72.00
plot(PLS_DALEX_Diagnostics,
variable = "y",
yvariable = "y_hat") +
geom_point(size=3) +
scale_x_continuous("Observed LIFEXP") +
scale_y_continuous("Predicted LIFEXP") +
geom_abline(slope = 1) +
ggtitle("PLS: Observed and Predicted LIFEXP")##################################
# Formulating the DALEX object
# for the Best CUBIST model
##################################
CUBIST_DALEX <- DALEX::explain(CUBIST_Tune,
data = MT.Model.Predictors,
y = MT$LIFEXP,
verbose = FALSE,
label = "CUBIST")
(CUBIST_DALEX_Performance <- model_performance(CUBIST_DALEX))## Measures for: regression
## mse : 4.252961
## rmse : 2.062271
## r2 : 0.9223281
## mad : 1.504065
##
## Residuals:
## 0% 10% 20% 30% 40% 50%
## -4.24762244 -2.06826235 -1.58225898 -1.15000147 -0.43114893 0.03237576
## 60% 70% 80% 90% 100%
## 0.34857635 1.11785478 1.58806246 2.95097520 6.41232629
(CUBIST_DALEX_Diagnostics <- model_diagnostics(CUBIST_DALEX))## GENDER INFMOR PERCAP CLTECH
## Male :43 Min. :0.5306 Min. :-1.4775 Min. : 0.20
## Female:29 1st Qu.:1.5623 1st Qu.: 0.8076 1st Qu.: 31.15
## Median :2.6602 Median : 1.5798 Median : 83.50
## Mean :2.5206 Mean : 1.7061 Mean : 67.12
## 3rd Qu.:3.4491 3rd Qu.: 2.8175 3rd Qu.:100.00
## Max. :4.4864 Max. : 4.2333 Max. :100.00
## NCOMOR y y_hat residuals
## Min. :2.511 Min. :53.79 Min. :55.96 Min. :-4.24762
## 1st Qu.:3.887 1st Qu.:67.49 1st Qu.:67.43 1st Qu.:-1.51031
## Median :4.685 Median :73.35 Median :72.64 Median : 0.03238
## Mean :4.736 Mean :72.42 Mean :72.32 Mean : 0.10287
## 3rd Qu.:5.631 3rd Qu.:78.39 3rd Qu.:78.58 3rd Qu.: 1.29561
## Max. :7.579 Max. :86.20 Max. :84.74 Max. : 6.41233
## abs_residuals label ids
## Min. :0.009592 Length:72 Min. : 1.00
## 1st Qu.:0.499008 Class :character 1st Qu.:18.75
## Median :1.504065 Mode :character Median :36.50
## Mean :1.591015 Mean :36.50
## 3rd Qu.:1.926649 3rd Qu.:54.25
## Max. :6.412326 Max. :72.00
plot(CUBIST_DALEX_Diagnostics,
variable = "y",
yvariable = "y_hat") +
geom_point(size=3) +
scale_x_continuous("Observed LIFEXP") +
scale_y_continuous("Predicted LIFEXP") +
geom_abline(slope = 1) +
ggtitle("CUBIST: Observed and Predicted LIFEXP")##################################
# Consolidating the performance
# on the model test data
##################################
plot(GBM_DALEX_Performance,
RF_DALEX_Performance,
NN_DALEX_Performance,
PLS_DALEX_Performance,
CUBIST_DALEX_Performance)plot(GBM_DALEX_Performance,
RF_DALEX_Performance,
NN_DALEX_Performance,
PLS_DALEX_Performance,
CUBIST_DALEX_Performance,
geom = "boxplot")plot(GBM_DALEX_Performance,
RF_DALEX_Performance,
NN_DALEX_Performance,
PLS_DALEX_Performance,
CUBIST_DALEX_Performance,
geom = "histogram")##################################
# Consolidating the variable importance
# on the model test data
##################################
GBM_DALEX_VariableImportance <- model_parts(GBM_DALEX,
loss_function = loss_root_mean_square,
B = 200,
N = NULL)
RF_DALEX_VariableImportance <- model_parts(RF_DALEX,
loss_function = loss_root_mean_square,
B = 200,
N = NULL)
NN_DALEX_VariableImportance <- model_parts(NN_DALEX,
loss_function = loss_root_mean_square,
B = 200,
N = NULL)
PLS_DALEX_VariableImportance <- model_parts(PLS_DALEX,
loss_function = loss_root_mean_square,
B = 200,
N = NULL)
CUBIST_DALEX_VariableImportance <- model_parts(CUBIST_DALEX,
loss_function = loss_root_mean_square,
B = 200,
N = NULL)
plot(GBM_DALEX_VariableImportance,
RF_DALEX_VariableImportance,
NN_DALEX_VariableImportance,
PLS_DALEX_VariableImportance,
CUBIST_DALEX_VariableImportance)##################################
# Summarizing the variable importance
# for the final model - GBM
##################################
GBM_DALEX_VariableImportance## variable mean_dropout_loss label
## 1 _full_model_ 1.868979 GBM
## 2 PERCAP 2.088901 GBM
## 3 GENDER 2.237822 GBM
## 4 CLTECH 2.334086 GBM
## 5 NCOMOR 4.002445 GBM
## 6 INFMOR 7.689843 GBM
## 7 _baseline_ 10.077581 GBM
plot(GBM_DALEX_VariableImportance)##################################
# Formulating the partial dependence plots
# for the final model - GBM
# using the different variables
##################################
GBM_DALEX_PartialDependencePlot_INFMOR <- model_profile(GBM_DALEX,
variables = "INFMOR")
GBM_DALEX_PartialDependencePlot_NCOMOR <- model_profile(GBM_DALEX,
variables = "NCOMOR")
GBM_DALEX_PartialDependencePlot_CLTECH <- model_profile(GBM_DALEX,
variables = "CLTECH")
GBM_DALEX_PartialDependencePlot_PERCAP <- model_profile(GBM_DALEX,
variables = "PERCAP")
(GBM_DALEX_PDP_INFMOR <- plot(GBM_DALEX_PartialDependencePlot_INFMOR,
geom = "profiles"))(GBM_DALEX_PDP_NCOMOR <- plot(GBM_DALEX_PartialDependencePlot_NCOMOR,
geom = "profiles"))(GBM_DALEX_PDP_CLTECH <- plot(GBM_DALEX_PartialDependencePlot_CLTECH,
geom = "profiles"))(GBM_DALEX_PDP_PERCAP <- plot(GBM_DALEX_PartialDependencePlot_PERCAP,
geom = "profiles"))##################################
# Formulating the grouped partial dependence plots
# for the final model - GBM
# using the different variables
##################################
GBM_DALEX_GroupedPartialDependencePlot_INFMOR <- model_profile(GBM_DALEX,
variables = "INFMOR",
groups = "GENDER")
GBM_DALEX_GroupedPartialDependencePlot_NCOMOR <- model_profile(GBM_DALEX,
variables = "NCOMOR",
groups = "GENDER")
GBM_DALEX_GroupedPartialDependencePlot_CLTECH <- model_profile(GBM_DALEX,
variables = "CLTECH",
groups = "GENDER")
GBM_DALEX_GroupedPartialDependencePlot_PERCAP <- model_profile(GBM_DALEX,
variables = "PERCAP",
groups = "GENDER")
(GBM_DALEX_GPDP_INFMOR <- plot(GBM_DALEX_GroupedPartialDependencePlot_INFMOR,
geom = "profiles"))(GBM_DALEX_GPDP_NCOMOR <- plot(GBM_DALEX_GroupedPartialDependencePlot_NCOMOR,
geom = "profiles"))(GBM_DALEX_GPDP_CLTECH <- plot(GBM_DALEX_GroupedPartialDependencePlot_CLTECH,
geom = "profiles"))(GBM_DALEX_GPDP_PERCAP <- plot(GBM_DALEX_GroupedPartialDependencePlot_PERCAP,
geom = "profiles"))